Manon Fourniol

Valentin Gies

Valentin Barchasz

Edith Kussener

Hervé Barthelemy

Remy Vauché

Hervé Glotin

Analog Ultra Low-Power Acoustic Wake-Up System Based on Frequency Detection

An ultra low power acoustic wake-up detector based on high frequency signal analysis is presented in this paper. Focused on environmental or military Internet of Things (IoT) applications, it aims at detecting in real time the presence of specific animal species or drones for generating alerts and for triggering power consuming tasks such as high frequency signal recording only when needed. This wake-up detector continuously monitors the presence of specific frequencies in an analog acoustic signal, with a good frequency selectivity and a high frequency detection capability. It is based on an ultra-low analog frequency to voltage converter using a current-mirror, analog timers and comparators. Dedicated to long term stealth environmental or military surveys, a strong emphasis has been put on power consumption reduction in order to limit size and weight of the system. This power consumption has been reduced to 34µW , leading to a full year of autonomy including the microphone when powered by 3 coin cell CR2032 batteries.

I. INTRODUCTION

Internet of Things (IoT) systems for environmental and military surveys are submitted to strong operational or environmental constraints, such as installing a device in a strict nature reserve or in a restricted area. Environmental and military surveys requires to record signals such as sound for long periods of time (several months or years). These recording operations are hardly compatible with low power and high frequency recording because of data storage capacity or battery energy.

For example, acoustic recording of animals such as bats or rats, having a communication and localization system emitting up to 120kHz [START_REF] Whitaker | National Audubon Society Field Guide to North American Mammals[END_REF] and aerial drones producing a characteristic noise around 40kHz [START_REF] Ganti | Implementation of detection and tracking mechanism for small uas[END_REF], requires to have a data acquisition system working at a minimum of 240ksps with a resolution of 24 bits for low level signal detection. This leads to store 2.5GB of data every hour for each channel.

In these conditions, it is almost impossible to have a portable autonomous long term data acquisition system able to record continuously. We have to cope with an issue described in [START_REF] Gestner | Glass break detector analog front-end using novel classifier circuit[END_REF]- [START_REF] Dobre | Low computational method for siren detection[END_REF]. In order to store efficiently the high frequency acoustic signals occurring sparsely at a random time, it is more efficient to start recording only when an interesting signal is present. This allows to avoid useless power consumption, useless data storage, and useless post-processing for rising alarms or finding out interesting events. Following the emerging idea of using edge computing techniques, this local prefiltering process allows to detect only interesting events, thus consuming less, saving a huge amount of data storage and making the recorder operational for a longer time.

Existing state of art environmental recording systems [START_REF] Gies | Qualilife-jason advanced sound-light recorder[END_REF] [7] are working on a standard 12V -1.2Ah lead battery having a global capacity of 14.4W h. In continuous high frequency recording, batteries would last only 30 hours, without including the energy necessary generating alerts. Fortunately, continuous recording is mostly not necessary for environmental rare event detection, but can be reduced to interesting data periods, for example for detecting intrusions of specific species in a strict natural reserve or malicious behaviors such as aerial drones invasions in a restricted military area.

A solution allowing long term monitoring and alert generation is to use a wake-up detector activated by detection of a specific frequency on the acoustic signal, and to record the signal only when the wake-up detector has been triggered. Low power wake-up systems based on raw frequency detection exist [START_REF] Alpman | Overview of low-power wake-up radio design insights[END_REF]- [START_REF] Höflinger | Smartphone remote control for home automation applications based on acoustic wake-up receivers[END_REF] but they are mainly specified on detecting a specific low frequencies [START_REF] Goldberg | A wake-up detector for an acoustic surveillance sensor network: algorithm and vlsi implementation[END_REF], [START_REF] Johansson | A simple precharged cmos phase frequency detector[END_REF] or are specialized in radio frequency analysis, for example RFID technologies [START_REF] Sanchez | Rfid based acoustic wake-up system for underwater sensor networks[END_REF]. There are also some detectors using advanced digital processing techniques and having more developed capabilities such as pattern detection, like [START_REF] Ge | Deep neural network based wake-up-word speech recognition with two-stage detection[END_REF] or [START_REF] Cauwenberghs | A miniature low-power intelligent sensor node for persistent acoustic surveillance[END_REF], [START_REF] Jeong | 21.6 a 12nw always-on acoustic sensing and object recognition microsystem using frequency-domain feature extraction and svm classification[END_REF], but with a limited sampling frequency or at a higher overall power cost due to analog to digital conversions.

In [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF], we have introduced a way for detecting environmental events using a digital wake-up detector working on high frequency sound signal has been proposed. Power consumption of this system is about 0.6mW . That is equivalent to the power consumption of the ultrasonic microphones used (Knowles SPU0410LR5H). Using a 14.5W h battery (classical 12V -1.2Ah lead battery), this micro-controller based wake-up system can be active about 3 years in continuous mode, compared with a 30 hours of continuous high frequency recording with most of the time no useful signal. This is paving the way for long term environmental survey protocols, but with a major drawback : it is impossible to use it in very lightweight and small embedded systems due to the lead battery size which is 10cm long and weights 600g.

In this paper, we present a novel implementation of a high frequency signal wake-up detector based on an analog implementation using discrete components as shown in Fig. 1.

Fig. 1. Ultra-low power analog wake-up detector First, the general architecture of the acquisition system is presented with a focus on the power consumption budget available for waking-up on interesting signal detection, having this detector work in always on mode. We call it the wake-up always on mode. In a second part, the ultra-low power analog implementation of the wake-up system based on frequency analysis is described. In a third part, power consumption results are presented and compared to existing solutions.

II. ACOUSTIC ENVIRONMENTAL MONITORING SYSTEM DESCRIPTION A. Size and weight reduction : a strong constraint on batteries

As explained in the introduction, compared to previous solutions, we aim at reducing the size and the weight of the data acquisition system to embed it anywhere without disturbing present species.

We mainly focus on the battery size reduction by using 3 standard 3V -0.23Ah-0.69W h CR2032 batteries (weighting 3g each) instead of a classical 12V -1.2Ah -14.4W h lead battery (weighting 600g) as presented in [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF]. Battery weight has to be reduced by a factor 100 whereas capacity has to be reduced by a factor 10. At the same time, lifetime of the detection system must be maintained in wake-up always on mode for at least one year. That means power consumption has to be reduced by approximately a factor 10 to compensate the battery reduction.

Considering that alerts to be detected and recorded are rare enough to be neglected, we can assume that the maximum acceptable average power consumption in wake-up detection mode is approximately equal to the battery capacity divided by the needed autonomy of the system (i.e. 12 months). It is the same for aerial drones detection systems, considering intrusions are rare. For example, using 3 coin cell batteries CR2032, this leads to allow an average power budget on one year of 3 × 0.69 24 × 365 = 236µW for the always on wake-up detection mode including the microphone power supply. With this constraint, use of the Knowles SPU0410LR5H microphone used in [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF] is not possible because its power consumption is 500µW . Instead, we have chosen another lower power microphone from Knowles, the FG-23329-P07 having a power consumption of about 200µW . This microphone has been validated experimentally for ultrasonic detection at 40kHz or more without any trouble, even if it is not described in the corresponding Knowles datasheet. With this microphone, 36µA are available for powering the wake-up always on detector.

B. System architecture

This paper focuses on the low power wake-up system based on frequency analysis, however a global description of the recording and analysis system is presented in Fig. 2. The acoustic low power wake-up system based on frequency analysis is connected with other parts of the high frequency recording system using a low power device. Processing is done in three steps, each one having an increasing power cost : Fig. 3. Low Power Acoustic Wake-Up System integrated in "Qualilife" recording system 1) First, a microphone captures the acoustic signal and transmits it to our low power analog wake-up detector based on frequency analysis. When the wake-up detects a specific frequency sound distinctive of an animal, an interrupt is sent to the management system. 2) Upon reception of an interrupt, the management unit microcontroller starts further advanced signal processing using traditional linear or non-linear filters, neural networks, wavelets transform or correlations to determine whether the signal detected is a true alert or not [START_REF] Kojima | Semi-automatic bird song analysis by spatial-cue-based integration of sound source detection, localization, separation, and identification[END_REF]- [START_REF] Chin | Audio event detection based on layered symbolic sequence representations[END_REF]. Other additional sensors can be used to improve the quality of these additional computations. These operations are performed on demand, with a more important power consumption of about 17.5mW . The management unit is also able to launch an alert. Theses filtering operations are not in the scope of this paper. 3) Then, if the detection is confirmed, the management unit starts recording the signal from the microphone and stores it on a SD card for a defined amount of time, in order to record the next occurrences of the interesting signal. It is important to notice that this strategy cannot be used for recording one shot signals because signal used for low power acoustic wake-up is lost. A solution using an analogical to digital signal buffer converter could be implemented but would not be low power.

III. ULTRA LOW-POWER WAKE-UP DETECTOR BASED ON FREQUENCY ANALYSIS ANALOG IMPLEMENTATION

The analog low power wake-up detector has been designed with an idea of simplicity in order to get the lowest power consumption possible. It is based on a frequency to voltage conversion, using a monostable driven linear discharge of a capacitor at a constant current. Periodically, the voltage of the capacitor, image of the input frequency, is sampled and hold for being compared to thresholds by analog comparators.

This implementation has been inspired by our previous digital implementation on a PIC24FJ256 [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF], [START_REF]/GB412 Family : 16-Bit Flash Microcontrollers with Dual Partition Flash Memory, XLP, LCD, Cryptographic Engine and USB On-The-Go[END_REF] from Microchip, but with a drastic reduction of power consumption.

A. Architecture Description

As shown on Fig. 4, signal from the analog low power microphone is first passed into an hysteresis comparator. This comparator allows ambient noise to be removed by triggering only when a the U S input signal have a sufficient amplitude. Then, output U h of the hysteresis comparator drives a monostable analog timer which will generate pulses U M with a constant high time, but synchronized with the input signal. At this step, we can notice that U M average value is directly proportional to the input signal U S frequency. At this step, a simple RC filter could be used, but the frequency to voltage conversion would not be linear due to the exponential charges of the RC filter. Instead of that, we need a perfectly linear conversion in order to determine accurately the input frequency.

This linear conversion is done using a current mirror coupled to a capacitor C 1 . This has been inspired by our preceding implementation as shown in [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF]. The current mirror is used like a Charge Time Measurement Unit (CTMU) [START_REF]Charge Time Measurement Unit (CTMU) and CTMU Operation with Threshold Detect[END_REF] present in Microchip micro-controllers. Pulses from the monostable timer create a constant current in the left transistor of the current mirror. This current is mirrored to get I C1 which discharges the capacitor C 1 . Thus, voltage U C1 decreases linearly during the monostable pulses, and remains constant during the other phases as shown in Fig. 5.

Equation 2 describes U C1 evolution, corresponding to waveforms presented at Fig. 5 :

U C1 = V CC - t 0 I C1 (t) C 1 dt = V CC - t 0 I 0 C 1 U m (t)dt = V CC - I 0 C 1 * N * T P W (1)
= V CC - I 0 C 1 * F S F SH * T P W (2)
where U m is a logical level 1 or 0 depending on the monostable output state, I 0 is the current during monostable pulses in the current mirror, T P W the pulses duration, N the number of pulses between two successive resets, F SH the sample and hold frequency, and F S is the input signal frequency. U C1 voltage is an image of the frequency and have to be read periodically. This is done using a low frequency astable timer producing periodically a signal U A at a fixed frequency F SH , for closing Q 1 PMOS transistor. U C1 voltage is sampled and hold into U C2 , considering that C 1 capacitor value is much more important than C 2 capacitor value. While this sample and hold operation has been done, Q 1 transistor is opened and Q 2 PMOS transistor is closed in order the reset the voltage U C1 value at V CC .

Consequently, U C2 voltage is an image of the input signal frequency. Final step of the process is to compare U C2 with fixed values in order to determine if the measured frequency is within or not a specified interval. An ADC could also be used to get the frequency value, but it would lead to a tremendous power consumption compared with the overall consumption of our wake-up trigger.

B. Implementation

Implementation of this ultra low-power wake-up detector has been done on a PCB board using ultra low power analog components. For example, 3.3V reference voltage is done using a ultra low power regulator TPS783 [START_REF]Ultralow Quiescent Current Low-Dropout Linear Regulator[END_REF] that regulates the input voltage to 3.3V while consuming only 500nA.

1) Input Comparators:

The input signal U S is first processed by a hysteresis trigger as shown in Fig. 4. The hysteresis comparator outputs the U h signal. A small ambient noise will not trigger the hysteresis comparator, thus eliminating the input low level perturbations. Compared to the single comparators used in [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF], the hysteresis comparator offers a more reliable noise rejection. In order to adapt filtering to the different detection sensitivity, hysteresis level can be adjusted by a reference voltage generated by the DAC of the supervisor.

Choice of the comparator is important because of the tradeoff between operating frequency and power consumption. TVL3691 [START_REF]TLV3691 0.9-V to 6.5-V, Nanopower Comparator[END_REF] is a good candidate with its very low power consumption (only 75nA). However, its maximum switching frequency (30kHz) is too low for high frequency operations such as bats detection (100kHz). Another reference has been chosen : TLV7031 [START_REF]TLV7031 and TLV7041 Small Size, nanoPower, Low-Voltage Comparators[END_REF] from Texas Instrument having a propagation delay comprised between 3µs and a supply current of 335nA. It allows to process signal up to 300kHz.

2) Timers: Ultra low power timers are a key for our analog wake-up detector. They are used for monostable pulses generation synchronized with the rising edges of the input signal, for astable sample and hold signal (U A ) generation and for generating a delayed signal (U R ) resetting capacitor voltage after a sample and hold operation. This requires the use of three timers. Due to power constraints, a very low implementation of the classical 555 timer has been chosen : the CSS555C [START_REF]CSS555C : Micropower Timer Datasheet[END_REF] consuming only 3µA and covering all our needs [START_REF]CSS555C : Application Circuits -Application Note[END_REF].

• Monostable timer : signal U h from the input comparators triggers a monostable timer, producing a rising edge U M on each falling edge of the input signal. Fig. 6 shows the hardware implementation using CSS555. Pulse width T P W is equal to (equation 3) : Fig. 6. Monostable Timer.

T P W = L(R A + 2R B )C T ( 3
)
where L = 0.695. In order to process input signals at 100kHz, T P W has been chosen equal to 0.66µs,

leading to R A = 10Ω, R B = 0Ω and C T = 100pF .
It is interesting to notice that lower capacitor values C T have been tested, but the pulse width was less precisely defined, due to parasitic capacitors not negligible under these values. • Astable low frequency timer : Astable signal is also generated by a CSS555 timer producing a periodic signal U A as shown in Fig. 7. Astable frequency is set using equation 4:

T OSC = L(R A + 2R B )C T (4)
where L = 0.695. In order to obtain a sample and hold timer frequency of about 1kHz, R A = 700kΩ, R B = 370kΩ and C T = 1nF have been chosen.

The capacitor reset signal U R is obtained for U A by generating a pulse with a delay using a CSS555 as shown in Fig. 8. Delay t D and pulse width t P W is set according to equation 5:

t D = 1 2 C T [t d0 (R A //R F + R B ) + (t d1 R B )] (5)
t P W = 1 2 C T L(R A //R F + 2R B ) (6)
where L = 0.695 and t d0 = ln( 23 )-ln(1-2 3 (R F +R A )/R F ). In order to have a delay of 20µs allowing to correctly transfer charges form C 1 to C 2 before reset, following values 3) Current Mirror: Current mirror is the last central element of our system. It is replacing the CTMU [START_REF]Charge Time Measurement Unit (CTMU) and CTMU Operation with Threshold Detect[END_REF], [START_REF] Yedamale | AN1375 : See What You Can Do with the CTMU[END_REF], [START_REF]Overview of Charge Time Measurement Unit (CTMU)[END_REF] used in [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF]. The current mirror is used as a constant controlled current source driven by the monostable output U M for discharging main capacitor C 1 . When activated, current value in the mirror I 0 is set by the serial resistor and the output voltage of the monostable as shown in equation 7.

I 0 = V CC -V BE R (7)
Monostable Timer

Mirror Current :

UC1 UM

Vcc

Trigger Delay

Fig. 9. Current Mirror Choice of the current mirror analog component is very important as precision of frequency measurement relies directly on the mirror precision. BCV62 PNP current mirror [START_REF]BCV62 PNP Silicon Double Transistor[END_REF] has been tested at a first time because of its low cost. But its precision was very low (70% of matching between transistor T1 and transistor T2) and not suitable for a precise detection of frequency. Then, REF200 [START_REF]REF200 Dual Current Source and Current Sink[END_REF] from Texas Instrument has been chosen. This expensive current mirror is very precise, with around 0.25% of matching between the two transistors. It works up to 5MHz which is more than sufficient for our 100kHz signal. Power consumption is not a problem for the mirror considering it is only consuming power when monostable is triggered, that means rarely. The choice of the REF200 has an impact on the design. Using NPN transistors, capacitor C 1 is being discharge by the mirror, whereas a PNP mirror could be used for charging the capacitor.

4) Capacitors and associated currents: Capacitor C1 is the heart of the analog wake-up detector, being charged linearly by successive phases. The values of C 1 , C 2 and I 0 have to be chosen carefully. Considering input frequency to be detected is 100kHz, sample and hold frequency is 1kHz, T P W = 0.66µs and U C1 minimal value is 0, equation 2 gives :

I 0 C 1 = V CC F S F SH * T P W = 50000 (8)
Limiting the mirror current to I 0 = 10µA conducts to have C 1 = 20nF .

After integration, U C1 voltage have to be sampled and hold in C 2 . This is done by closing Q 1 PMOS. Charges are transferred from the C 1 to C 2 , leading to a change in U C1 . In order to keep this change a low as possible, C 2 value have to be chosen far lower than C 1 and has been set to C 2 = 1nF .

5) Output Comparators: Once the U C1 has been sampled and hold into C2 capacitor, U C2 has to be exploited in a very efficient and simple way. This is done using ultra low power and low speed comparators as shown on Fig. 10. By associating 2 comparators, a specific range of input frequency can be selected in output with a good precision. If the input signal frequency is in the selected interval, both outputs of the comparators will be equal to a logical 1, triggering an interrupt on the management unit.

Several comparators associations can be done, allowing to detect a signal in several bands of frequencies. This can be used for detecting generated frequencies in order to synchronize or trigger a wireless sensor network as presented in [START_REF] Fourniol | Ultra low-power analog wake-up system for environmental iot networks and military smart dust[END_REF].

As the output comparators are working at a low frequency, is not necessary to use a fast comparator such as the TVL7031. Instead of that, the TVL3691 can be used, leading to an additional power reduction.

IV. RESULTS

Our ultra-low power wake-up detector has the ability of detecting signals having a frequency up to 300kHz. As we can see on Fig. 4, three comparators TLV7031 are present in the design, each one having a power consumption of 0.335µA. Three analog functions using a CSS555C are also used, each one having a power consumption of about 3µA under 3.3V . Power consumption of the current mirror is very low, because this component is only active on the very short monostable pulses, with an active current limited to 10µA. This is also true for the resistors around the CSS555C. Both MOSFETs are also used sparsely and thus have a very limited power consumption compared to the CSS555C. Adding all power consumptions, this leads to an approximate overall consumption of 36µW for the analog wake-up detector.

This power consumption can be reduced by replacing the TLV7031 by the TLV3691 which consume only 75nA but is limited to 30kHz. This replacement can be done only on the final comparators considering they are working at a low frequency (less than 1kHz). We this replacement, power consumption is reduced to 34µW .

This power consumption can be compared with the microcontroller implementation of the acoustic Wake-up detector presented in [START_REF] Fourniol | Low-power wake-up system based on frequency analysis for environmental internet of things[END_REF]. Its power consumption is about 600µW . That means our analog wake-up detector power consumption is 18 times lower than this previous implementation. This allows the system to detect high frequency analog wake-up events in continuous mode during 2.3 years on a single CR2032 coin cell having a 230mAh capacity, without considering the microphone. Used with a low power microphone FG-23329-P07 from Knowles, the power consumption raises to 230µW , leading to a continuous detection of 125 days using a single CR2032 coin cell. A one year autonomy can be achieved using 3 coin cells, having a total weight of 9 grams. [START_REF] Glotin | Analyse temps-réel de masse acoustique pour le suivi d'espèce nuisible à port-cros[END_REF].

V. CONCLUSION AND FUTURE WORKS

The ultra-low power (34µW ) always-on wake-up detector presented in this paper is lightweight and small. It can be used for applications in environmental or military surveys such as stealth intelligent recording of acoustic signals emitted by animals or drones. Capable of detecting high frequency acoustic signals in a very low power mode, it is used as a prefiltering stage for detecting instants when an input signal having a chosen frequency is present or suspected to be there. This solution is perfectly suited for long term embedded stealth monitoring systems used in environment or military applications. This analog wake-up detector shown in Fig. 1 is used for protecting a strict nature reserve island (Ilot Bagaud, part of the Parc National de Port Cros in France) from rats intrusions, combined with the Qualilife system [START_REF] Barchasz | Technical report on high frequency recording systems[END_REF], [START_REF] Glotin | Analyse temps-réel de masse acoustique pour le suivi d'espèce nuisible à port-cros[END_REF] for recording, analyzing and transmitting alerts in real time.

Other applications using non acoustic high frequency signal analysis and detection can be developed using our analog Wake-up detector. In particular, using photo-transistors instead of microphones allows to have a pulsed light wake-up system operated from a long distance with no propagation delay. Triggering simultaneously a whole environmental wireless sensor network with this solution is possible at a very low power consumption. This solution is reliable even in a dense environment such as a rain forest. This can be used in cooperation with sound detection for trajectography applications [START_REF] Fourniol | Ultra low-power analog wake-up system for environmental iot networks and military smart dust[END_REF] in environmental surveys.

Fig. 2 .

 2 Fig. 2. Global recording system description.

Fig. 4 .

 4 Fig. 4. Ultra Low Power Acoustic Wake-Up System

Fig. 5 .

 5 Fig. 5. Analog Wake-up detector waveforms

Fig. 7 .

 7 Fig. 7. Astable Timer.

Fig. 8 .

 8 Fig. 8. Delay Timer.

Fig. 10 .

 10 Fig. 10. Final Computation

ACKNOWLEDGMENT

The authors would like to thank the Parc National de Port Cros in France for their cooperation in the development and use of our recording systems and acoustic wake-up systems.

The authors would particularly like to thank the Embedded Electronics Technology Platform of Toulon University named SMIoT (Scientific Microsystems for Internet of Things) [37] for its support in the electronic achievement of the project.


Graduate Student Member, IEEE Guillaume Tochou
email: guillaume.tochou@st.com

Member, IEEE Robin Benarrouch
email: robin.benarrouch@st.com

Member, IEEE David Gaidioz
email: david.gaidioz@st.com

Senior Member, IEEE Andreia Cathelin
email: andreia.cathelin@st.com

Antoine Frappé
email: antoine.frappe@junia.com

Andreas Kaiser
email: andreas.kaiser@junia.com

Life Fellow, IEEE Jan Rabaey
email: janrabaey@berkeley.edu

s Pulse-based Digital Transmitter for the Human Intranet in 28 nm FD-SOI CMOS

Keywords: Body Area Network (BAN), Ultra-Low Power Transmitter, Ultra-Low Voltage (ULV), Transmitter, Forward Body Biasing (FBB), Body-Coupled Communication (BCC), 28 nm FD-SOI CMOS

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

T HE Human Intranet (HI) aims to interconnect various kinds of sensors and actuators on the human body [START_REF] Rabaey | The Human Intranet-Where Swarms and Humans Meet[END_REF]. Such a network is finding applications in wearables and implantable devices for wellness and health purposes such as hearing-aid, continuous glucose monitoring (CGM), electrocardiogram (ECG), oximeter, electromyogram (EMG), brainmachine interface (BMI), among others. In a more long-term view, such a network could possibly become a way to augment ourselves to cope with a quick-changing environment [START_REF]Human-centric computing[END_REF].

Today's wearables such as smartwatches and earbuds are usually connected to a smartphone with Bluetooth Low Energy (BLE) technology. This implies a centralized network almostfully relying on the smartphone which is not robust enough and sensibly limits the battery lifetime to a couple of days. Moreover, BLE technology limits the number of linked devices (up to 7 devices in the latest Bluetooth 5.0 version) and undergoes a limited maximum data rate of 1 or 2 Mb/s. Such data rates are fairly enough for audio and sensor applications (ECG, EMG, etc.) but are too low for more complex applications such as BMI and prosthesis control.

The purpose of the Human Intranet is to create a decentralized network where all types of on-body devices with diverse data rates can interact with each other. For a "user-friendly" experience, the devices need to have a small form-factor for comfortable wearability. To avoid battery replacement or allow battery-less devices, the power consumption has to be as low as possible for a long-term operating life. It is also required to reach "long-range" (at the human body scale) communication to avoid the use of repeaters that would degrade the wearability and power consumption of the network. Finally, the network requires a robust communication scheme when undergoing changing environments and motions. We can then identify 5 main requirements for the design of Human Intranet transceivers :

• Flexible data rate • Wearability/Form factor • Power consumption/Energy efficiency • Range/Body coverage • Robustness On a higher level, it is important to note that privacy, security, and safety cannot be neglected at any cost [START_REF] Sen | The body is the network: to safeguard sensitive data, turn flesh and tissue into a secure wireless channel[END_REF].

In [START_REF] Solt | Energy Efficient Heartbeat-Based MAC Protocol for WBAN Employing Body Coupled Communication[END_REF] a decentralized network topology was proposed. It presents a "mesh-of-star" topology where Hubs are interconnected with each other in a mesh topology and the Leaves are connected in a star topology with a Hub as the local central node (Fig. 1). Hubs are defined as the nodes generating an important amount of data (e.g. BMI, prosthesis control, etc.), with low latency and more computing capability, embedded intelligence, and available energy. On the other hand, Leaves are defined as nodes generating a lower amount of data, such as temperature and chemical sensors, ECG, etc., and are ©IEEE Version submitted for final publication in IEEE Journal of Solid-State Circuits Hub Leaf H i g h D a t a R a t e L o w D a t a R a t e Fig. 1. The Human Intranet network [START_REF] Solt | Energy Efficient Heartbeat-Based MAC Protocol for WBAN Employing Body Coupled Communication[END_REF]. much more energy-constrained. Hence, Hubs require a high communication data rate (tens of Mb/s) where Leaves require lower data rates (hundreds of kb/s) to save power.

This work presents an ultra-low power transmitter suitable for both Hubs and Leaves with a flexible data rate for the Human Intranet. This paper presents an extended version of [START_REF] Tochou | A Fully-Digital 0.1-to-27 Mb/s ULV 450 MHz Transmitter with sub-100µW Power Consumption for Body-Coupled Communication in 28 nm FD-SOI CMOS[END_REF]. The paper is structured as follows : Section II presents the state of the art in Body Area Network (BAN) and how the different existing solutions cope with the requirements stated in the introduction. Section III presents the system principles of the proposed transmitter. Section IV describes the circuit implementation. Section V presents the measurement results and compares this work with the state of the art. Section VI presents the on-body demonstration of the propagation mechanism with the transmitter. Finally, Section VII concludes this work.

II. STATE OF THE ART IN BODY AREA NETWORK A. Free-space RF communication

As stated in the introduction, BLE is a very widely used standard making the devices easily compatible one with another, but suffers from limited data rates and most of the time with important power consumption [6], [START_REF] Shi | 28.3 A 606µW mm-Scale Bluetooth Low-Energy Transmitter Using Co-Designed 3.5× 3.5 mm 2 Loop Antenna and Transformer-Boost Power Oscillator[END_REF]. Another extensively studied solution using RF propagation for BAN is Ultra-Wideband (UWB). The principle of UWB lies in a pulse-based communication in the 3.1-10.6 GHz band. Each bit is transmitted by a short-time pulse allowing bit-level duty cycling for reduced power consumption, especially at low data rates. Simple modulations are usually used such as OOK, PPM, and BPSK. OOK and PPM allow using non-coherent receivers reducing the constraints and power consumption by using a PLL-free architecture. This communication technology has demonstrated its ability to reach high data rates and its energy efficiency [START_REF] Streel | SleepTalker: A ULV 802.15. 4a IR-UWB transmitter SoC in 28nm FDSOI achieving 14 pJ/b at 27 Mb/s with channel selection based on adaptive FBB and digitally programmable pulse shaping[END_REF]- [START_REF] Norimatsu | A UWB-IR transmitter with digitally controlled pulse generator[END_REF].

However, communicating over the air around the human body is challenging because of the body shadowing effect. When communicating on-body or around-body, the human body absorbs the radio signal when the transmitter and the receiver are non-line-of-sight (e.g. for a torso to back communication). This degrades the path loss and makes the channel unstable with motion and varying environments. To compensate for the body shadowing effect, it would require increasing the output power of the transmitter, improving the sensitivity of the receiver, using larger gain antennas, and/or using repeaters. Such solutions would inevitably increase the power consumption and/or downgrade the wearability of the nodes.

B. In-Body Ultrasound

As the human body is mostly composed of water ultrasounds propagate easily in the human body. Hence this communication technique has been recently studied for human body communication. However, the channel suffers from multipath caused by the reflections of the heterogeneous medium composed of muscle, bone, and skin material [START_REF] Bos | Ultrasound in-body communication with OFDM through multipath realistic channels[END_REF]. Furthermore, similarly to electromagnetic communication, losses increase with the frequency which limits the central frequency of the transducers to ∼10 MHz. This frequency limitation inherently restrains the bandwidth and hence the communication data rate [START_REF] Bos | Ultrasound in-body communication with OFDM through multipath realistic channels[END_REF]. Ultrasound remains a good communication option for short-range implant (under-skin) to on-body (onskin) communication with a low data rate.

C. Body Coupled Communication

To overcome the body shadowing effect, a recently studied alternative solution is body coupled communication (BCC). This solution proposes to use the human body as the communication channel instead of free-space. Three different mechanisms have been mainly studied so far in BCC [START_REF] Yoo | Body coupled communication: Towards energy-efficient body area network applications[END_REF] :

1) Magnetic BCC (m-BCC): This mechanism is using resonant coils wrapped around body limbs to communicate through magnetic fields. Magnetic fields travel with low loss through biological tissue, as the human body is magnetically inert [START_REF] Park | Magnetic human body communication[END_REF]. Hence, if the magnetic resonance is kept constant, magnetic coupling reaches low path loss with all body coverage. This allows reaching ultra-low power transceivers with low output power at the TX side and relaxed sensitivity at the RX side [17]. However, coils wrapped around the body are not easily wearable. For example, having a coil around the head or the torso is not convenient. Furthermore maintaining magnetic resonance is easily sensitive to motion artifacts. It remains a good communication option if both the receiver and transmitter are on the same limb with light coils.

2) Galvanic BCC (g-BCC): This communication scheme uses two electrodes on both the transmitter and the receiver. The principle is based on a differential signal creating a galvanic current propagating thru the skin. Galvanic coupling has the advantage to be very robust to environment changes due to a very stable channel. However, it suffers from strong path loss beyond a few centimeters [START_REF] Callejon | Distributed circuit modeling of galvanic and capacitive coupling for intrabody communication[END_REF]. Furthermore, the low frequency of operation limits the usable bandwidth and hence the data rate. Thus, this makes g-BCC a good candidate for an implant (under-skin) to on-body (on-skin) communication, but makes full-body coverage communication unachievable. 3) Capacitive BCC (c-BCC): the principle is based on one electrode in contact with the skin at the TX and RX side, with a floating electrode on each side acting as ground electrodes. The signal propagates along the body as an electric field. The return path is formed by the ground and air environment creating a capacitive coupling between the transmitter and the receiver [START_REF] Bae | The signal transmission mechanism on the surface of human body for body channel communication[END_REF]. The dominant propagation mechanism in capacitive body coupled communication depends on two parameters : frequency and distance (Fig. 2). For a higher frequency and/or longer distance the dominant propagation mechanism is the surface wave (SW) propagation. On the other hand, at a lower frequency or very short distances, electro quasi-static (EQS) coupling will be the dominant mechanism. c-BCC with a frequency of operation below 100 MHz offers low path loss for a distance up to 1.5 m [START_REF] Bae | The signal transmission mechanism on the surface of human body for body channel communication[END_REF]. However, because of the important influence of the return path on the channel, the communication can be sensitive to changing environment and motion when EQS is the dominant propagation mechanism.

Capacitive body-coupled communication in the 400-500 MHz band has been studied in [START_REF] Thielens | A comparative study of on-body radiofrequency links in the 420 MHz-2.4 GHz range[END_REF], [START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF], with the main idea to increase the carrier frequency and the available bandwidth and hence the data rate. At 450 MHz the path loss can be approximated with EQS near-field propagation for up to 15 cm. At a further distance, surface wave propagation becomes predominant and the path loss is approximated by far-field propagation (Fig. 2). [START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF] demonstrated with numerical simulation and measurements that far-field losses have a slope of -0.37 dB/cm at 450 MHz which is approximately the same slope value of a near-field EQS propagation at 80 MHz found in [START_REF] Bae | The signal transmission mechanism on the surface of human body for body channel communication[END_REF]. The two models: EQS propagation at 80 MHz and surface wave model at 450 MHz are compared in Figure 3. These two models are also compared to a UWB around-body model [START_REF] Fort | Ultra wide-band body area channel model[END_REF] to emphasize how UWB, and more generally over-the-air radio transmissions, suffers from the body shadowing effect, causing important path loss compared to c-BCC. The path loss models are normalized at 5 cm distance from the antenna/electrode as the insertion loss (loss at 5 cm distance) depends on the antenna/electrode impedance matching. Furthermore, [START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF] compared to c-BCC EQS [START_REF] Bae | The signal transmission mechanism on the surface of human body for body channel communication[END_REF] and around-body UWB [START_REF] Fort | Ultra wide-band body area channel model[END_REF] (Note: The path loss models are normalized at 5 cm distance from the antenna/electrode as the insertion loss depends on the antenna/electrode impedance matching).

[21] highlighted a minimum available bandwidth of 100 MHz at 450 MHz over which the attenuation is almost to be constant. Ultimately, since the propagation mechanism is dominated by surface waves phenomena at 450 MHz, a system implementation at this frequency of operation enables a communication less sensitive to environment changes since the return path has almost no impact at this frequency of operation [START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF].

Several transceiver implementations have been proposed in the state of the art using capacitive body coupled communication [START_REF] Maity | BodyWire: A 6.3-pJ/b 30-Mb/s-30-dB SIR-tolerant broadband interference-robust human body communication transceiver using time domain interference rejection[END_REF]- [START_REF] Cho | A 79 pJ/b 80 Mb/s Full-Duplex Transceiver and a 42.5µW 100 kb/s Super-Regenerative Transceiver for Body Channel Communication[END_REF]. All of them use frequencies below 150 MHz where the EQS propagation mechanism is predominant. [START_REF] Maity | BodyWire: A 6.3-pJ/b 30-Mb/s-30-dB SIR-tolerant broadband interference-robust human body communication transceiver using time domain interference rejection[END_REF], [START_REF] Lee | A 16.6-pJ/b 150-Mb/s body channel communication transceiver with decision feedback equalization improving¿ 200% area efficiency[END_REF] proposed transceivers with direct-baseband communication with up to 30 Mb/s and 150 Mb/s, respectively. Such solutions allow using simple driving buffers at the transmitter side and defer the complexity to the receiver. Although both solutions report good energy efficiency below 20 pJ/b, the reported power consumption does not take into account the clock generation power consumption which would be the most power-consuming part. [START_REF] Chatterjee | A Context-aware Reconfigurable Transmitter with 2.24 pJ/bit, 802.15. 6 NB-HBC and 4.93 pJ/bit, 400.9 MHz MedRadio Modes with 33.6% Transmit Efficiency[END_REF], [START_REF] Zhao | A low-power compact IEEE 802.15. 6 compatible human body communication transceiver with digital sigma-delta IIR mask shaping[END_REF] proposed transceivers compliant with the IEEE 802.15.6 BAN standard section dedicated to human body communication [START_REF]IEEE Standard for Local and metropolitan area networks -Part 15.6: Wireless Body Area Networks[END_REF]. In this standard section, the frequency of operation is defined at 21 MHz with a 3 dB bandwidth of 5.25 MHz with a constrained mask-shaping requiring a sharp rejection below 2 MHz [START_REF] Zhao | A low-power compact IEEE 802.15. 6 compatible human body communication transceiver with digital sigma-delta IIR mask shaping[END_REF]. Such low frequency of operation and bandwidth limits the maximum achievable data rate to a few Mb/s. [START_REF] Cho | A 79 pJ/b 80 Mb/s Full-Duplex Transceiver and a 42.5µW 100 kb/s Super-Regenerative Transceiver for Body Channel Communication[END_REF] proposed a dual-transceiver being able to communicate efficiently at both high (80 Mb/s) and low data rate (100 kb/s). The high data rate transceiver uses a dual-band communication with BPSK modulation in the 20-60 MHz and 140-180 MHz bands to avoid FM-band interference (80-100 MHz).

III. SYSTEM CONSIDERATIONS OF THE PROPOSED

TRANSMITTER

According to the channel studies in the 400-500 MHz band [START_REF] Thielens | A comparative study of on-body radiofrequency links in the 420 MHz-2.4 GHz range[END_REF], [START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF], this allows for wide bandwidth >100 MHz and more channel stability under motion and changing environment. Taking advantage of the wide available bandwidth, the TX is inspired by UWB principles with the use of pulsebased communication with simple OOK modulation. Pulsebased communication can handle aggressive duty-cycling transmitters allowing for low power consumption and the ability to reach high data rates. From the analysis in [START_REF] Marchaland | System concepts dedicated to UWB transmitter[END_REF], the trade-off between bandwidth (BW) and maximum data rate is defined. The temporal expression of a pulse centered at the frequency f c with a Gaussian envelope can be written as:

p(t) = A exp - t 2 2σ 2 cos(2πf c t) (1)
where A is the maximum amplitude of the pulse and σ the standard deviation of the Gaussian envelop. In the frequency domain the single side-band Fourier transform of (1) gives:

P + (f ) = A √ πσ exp - (2πσ(f -f c )) 2 2 (2)
The trade-off between the pulse width (T p =∼ 6σ), and hence the maximum pulse repetition frequency (P RF =∼ 1/6σ), and the bandwidth (BW) at -10 dBc is given by solving :

10 log 10 |P + (f )| 2 |P + (f c )| 2 f =fc±BW/2 = -10 dB (3)
This gives the following relationship between BW and PRF:

BW (P RF ) = -ln(0.1) • P RF π ( 4
)
This function is plotted in Figure 4. As a compromise between available bandwidth and maximum achievable data rate, the chosen bandwidth is 150 MHz, which corresponds to a ∼20 ns pulse. For a 1 bit per pulse modulation like OOK, this is corresponding to a maximum theoretically achievable data rate of ∼50 Mb/s. A shorter pulse would require more bandwidth, while a longer pulse would limit the maximum achievable data rate. Furthermore, as OOK modulation does not require specific phase noise requirements as it is an amplitude modulation, short pulses with this simple modulation enable the use of an unlocked free-running oscillator, avoiding the use of a PLL which is a power-consuming part in RF circuits. The chosen frequency is not covered by the IEEE 802.15.6 standard for c-BCC communication, hence there is no specific spectrum mask to fulfill. However, as the fractional bandwidth is more than 20 % we can consider the signal as a UWB signal, which should respect FCC regulation [START_REF]First Report and Order[END_REF]. Although the emitted signal would be mainly confined to the body, the signal will partially radiate because of the body antenna effect, where the human body acts as an antenna radiating the signal power into the air [START_REF] Cho | The human body characteristics as a signal transmission medium for intrabody communication[END_REF]. The impact of the body antenna effect has not been measured yet for frequencies higher than 150 MHz, but this effect is expected to increase with frequency. Hence, it is desirable to limit out-of-band emissions, for example in the GPS band (960-1610 MHz) [START_REF]First Report and Order[END_REF].

The transmitter has been designed for a 50 Ω load for measurements purposes. In a complete solution, specific electrodes matched to the desired impedance should be designed. Regarding the receiver requirements, these have been addressed in [START_REF] Solt | Energy Efficient Heartbeat-Based MAC Protocol for WBAN Employing Body Coupled Communication[END_REF]. From the system analysis with classical RF models, it is estimated that a receiver with a sensitivity of -104 dBm and -77 dBm, at low and high data rates respectively, would allow for communication up to 50 cm on-body.

IV. TRANSMITTER IMPLEMENTATION

The proposed TX circuit (Fig. 5) is composed of a freerunning pseudo-differential ring oscillator (PD-RO) at 90 MHz feeding a switched-capacitor power amplifier (SCPA) with embedded edge-combiners (EC), in order to reach the 450 MHz central frequency. The overall TX is data-enabled (OOK modulation), which means it only runs during the pulse duration, saving power at low data rates. Short pulses combined with non-coherent OOK modulation accept the use of an unlocked frequency reference and enable instant startup time and aggressive duty-cycling. A low-frequency oscillator with frequency multiplication in the power amplifier allows for reduced dynamic power consumption as the circuit is massively low-frequency operated. Digital pulse-shaping is also introduced for spectral efficiency limiting out-of-band emissions. The 28 nm FD-SOI technology allows for threshold voltage tuning through wide range body-biasing. As depicted in Figure 6, in the 28 nm FD-SOI technology, the body factor is ∼85 mV/V for the thin oxide LVT devices and the body voltage variation can range up to +/-3 V [START_REF] Clerc | The Fourth Terminal: Benefits of Body-Biasing Techniques for FDSOI Circuits and Systems[END_REF]. This feature is used in three folds in the proposed circuit. First, it enables low threshold voltage allowing ultra-low voltage operation down to 0.5 V for ultra-low power consumption. Secondly, it allows for frequency calibration and process compensation on the oscillator, by tuning the threshold voltage of the inverters in the ring oscillator. Thirdly, it enables power amplifier efficiency optimization by reducing the ON-resistance of the transistors. The oscillator's and SCPA's output inverters are custom designed with a gate length increased to L min + 16 nm as well. This relaxed gate length limits the impact of leakage and offers manufacturing robustness while operating at 0.5 V. The one-time calibration, through body-biasing, of the PD-RO and the EC-SCPA is made externally. In future work, a complete SoC implementation with integrated body-bias generator [START_REF] Blagojević | A fast, flexible, positive and negative adaptive body-bias generator in 28nm FDSOI[END_REF] and periodic calibration with an on-chip reference should be designed. Such additional parts will certainly add power consumption overhead to the transmitter. However, an integrated body-bias generator would allow to duty-cycle the body-biasing by the input data like the rest of the transmitter. Hence, it will decrease the static power consumption due to leakage currents. Similarly to the body-bias generator, voltage regulation shall be included in a complete SoC implementation.

A. Pseudo-Differential Ring Oscillator

A ring oscillator requires at least two periods to oscillate. Hence, the 90 MHz frequency is chosen as two periods correspond to 22.22 ns which is the specified pulse width. A pseudo-differential architecture is composed of two singleended ring oscillator loops mounted in a pseudo-differential topology using inverter-based latch locking between each stage. The PD-RO circuit is represented in Figure 7. The oscillator is based on 45 stages pseudo-differential architecture. 5 phases are taken every 9 stages from both the negative and positive loops to perform frequency multiplication. The PD-RO is designed with an important number of stages to reach low-frequency of oscillation and to enable symmetric dutycycling for proper edge-combining. The positive feedback enables fast current transitions with sharp edges as required for the edge combination [START_REF] Gaidioz | Ring VCO Phase Noise Optimization by Pseudo-Differential Architecture in 28nm FD-SOI CMOS[END_REF]. A NAND gate inside each loop duty-cycles the oscillator according to the input data.

The PD-RO low-frequency reduces the power consumption of the frequency reference down to 11 µW when running at 90 MHz. As presented in Figure 8.a, the oscillator frequency fine-tuning is obtained via body-biasing through a one-time calibration (around VBB RO = 0.5 V), and to compensate process variations between different devices. As the PD-RO is turned-off between each pulse by the introduced NAND gates, there is no long-term drift of the oscillator. According to jitter theory in free-running ring oscillator, for a short runtime, white noise is the dominant noise source in free-running oscillators, and jitter increases as the square root of the run-time [START_REF] Mcneill | Jitter in ring oscillators[END_REF]. Hence by measuring the phase noise of the oscillator in the -20 dB/decade slope the equivalent accumulated jitter can be calculated. In Figure 8.b we present the measured PD-RO phase noise. The PD-RO achieves a measured accumulated jitter of 74 ps when running for a 22.22 ns duration.

B. Edge-Combiners Switched-Capacitor Power Amplifier

As presented in Figure 9.a and 8.c the edge-combiners (EC) are performing frequency multiplication by 5 to reach the 450 MHz central emission frequency. In each SCPA slice, an edge combiner multiplies the PD-RO frequency by using the 5 phases of the 90 MHz oscillator. Having the edge combiner distributed in each of the 16 slices allows having the 450 MHz frequency only at the very final inverter of each slice allowing a massively low-frequency operated circuit. It also relaxes the constraints on buffers and 'clock' tree distribution, with the multiplication performed only in the activated slices. In practice, the conceptual AND and OR gates represented in Figure 9.a are implemented in NAND logic for fast switching, limiting the introduced jitter by the edge-combiners. As observable in Figure 11, the edge-combiners provide robust frequency-multiplication thanks to body-biasing. Applying a 2.2 V body biasing on the edge-combiners reduces by 40X the standard deviation of process and mismatch variations compared to a nominally body-biased edge-combiner. This effect is explained by the threshold voltage reduction offered by FBB, making the switching of the NAND gates sharper and hence reducing timing errors.

The edge combiner drives an output inverter in series with a 135 fF capacitance acting as a switched capacitor power amplifier. CMOS inverter-based SCPA allows for high integrability, digital configuration, and high efficiency [START_REF] Yoo | A switched-capacitor RF power amplifier[END_REF]. The energy-efficiency of an SCPA strongly depends upon the ON-resistance (r on ) of the switches in the output inverters. The usual technique to reduce r on is to increase the width of the transistors. However, this leads to an increase of the parasitic capacitance, increasing the dynamic power consumption and hence degrading the efficiency of the power amplifier. The forward body-biasing (FBB) knob, specific to FD-SOI technologies, is used to lower the threshold voltage of the transistors, reducing r on at the low 0.5 V supply voltage, limiting their size and parasitic capacitance which improves the efficiency of the power amplifier as well as saving area. By using the simulation-validated model proposed in [START_REF] Tochou | Impact of Forward Body-Biasing on Ultra-Low Voltage Switched-Capacitor RF Power Amplifier in 28 nm FD-SOI[END_REF] it is shown in Figure 10 that a forward body-biased SCPA improves by 6 % the peak system efficiency (SE peak ) of the power amplifier while reducing by 45 % the switches area at 450 MHz. For further area saving the unit capacitances C u in each slice are MOM capacitances stacked on top of the inverters.

From SCPA theory [START_REF] Yoo | A switched-capacitor RF power amplifier[END_REF], for an ideal peak output power of 0 dBm and a supply voltage of 0.5 V, the optimal load is equal to 50 Ω which avoids the use of a lossy matching network. An external inductance is used in a 0402 package to form a wideband LC bandpass filter centered at 450 MHz.

C. Digital Pulse Shaping

As observable in Figure 12, the pulse shape has an important impact on the frequency response of the transmitter. With a rectangular pulse shaping (i.e. no pulse shaping) important sidelobe emissions are observable which cause interferences and degrade spectral efficiency. Gaussian pulse shaping has already been demonstrated to be very efficient to limit outof-band spectral emissions [START_REF] Streel | SleepTalker: A ULV 802.15. 4a IR-UWB transmitter SoC in 28nm FDSOI achieving 14 pJ/b at 27 Mb/s with channel selection based on adaptive FBB and digitally programmable pulse shaping[END_REF], [START_REF] Marchaland | System concepts dedicated to UWB transmitter[END_REF]. Similarly to [START_REF] Streel | SleepTalker: A ULV 802.15. 4a IR-UWB transmitter SoC in 28nm FDSOI achieving 14 pJ/b at 27 Mb/s with channel selection based on adaptive FBB and digitally programmable pulse shaping[END_REF], Figure 12 compares ideal Gaussian pulse shaping with sampling frequencies equal to f c and 2f c . It is shown that an ideal Gaussian pulse shaping at 2f c sampling frequency removes the sidelobes emissions at 2f c , limiting out-of-band emissions. Hence, a near-Gaussian pulse shaping, as shown in Figure 9.d, with a sampling frequency of 2f c is implemented. In Figure 9.b the schematic of the implemented pulse-shaper The 2f c frequency is then easily generated without power consumption overhead by using the complementary clocks, thanks to the pseudo-differential architecture of the oscillator. The frequency response of the near-Gaussian pulse shaping implemented is observable in Figure 12, and shows that it limits out-of-band emissions at 2f c frequency and improves spectral efficiency. The pulse shaper and edge combiners are designed using standard cells with a poly-biasing (i.e. gate length extension) of 16 nm. The digital pulse-shaper, edgecombiners, and associated logic are body-biased with the same voltage as the power amplifier (VBB P A ) for fast-switching at low operating voltage.

V. MEASUREMENTS RESULTS

The presented chip has been implemented in the 28 nm FD-SOI CMOS technology from STMicroelectronics, as presented in Figure 13. For measurements, LDOs are placed on PCB for voltage regulation at 0.5 V. The proposed transmitter reaches a maximum achievable data rate of 27 Mb/s. The output power and system efficiency of the TX is measured on several devices at the maximum data rate (for Hub-to-Hub communication) and at a low data rate of 100 kb/s (for Leaves to Hub communication). In Figure 14.a the output power and system efficiency (at low and high data rates) is plotted versus the applied body-biasing voltages on the power amplifier. It shows that for a measured -19.7 dBm output power at the maximum data rate of 27 Mb/s, the EC-SCPA reaches an optimal operating point at VBB P A = 2.2 V where the system efficiency is maximum reaching 14 %. An optimal body biasing point exists as FBB decreases the ON-resistance, while on the other hand, it increases the current leakage. Hence, the optimal bias corresponds to the point where the leakage power consumption becomes predominant. The body-biasing voltage can be adapted to reach the best system efficiency for a given bandwidth and the effect of the pulse shaping. We can notice in Figure 15.b spurs spaced by 27 MHz which corresponds to the pulse repetition rate. However, the frequency response is better at f c ≈ 350 MHz (VBB RO = 0.4 V) as observable in Figure 15.b. This is due to a slight mismatch of the LC filter at the output, because of parasitics. A slight change of the LC bandpass could result in a performance improvement in terms of output power and efficiency at 450 MHz and remove the spurs in the 300-400 MHz band. The pulses reach a peak-to-peak amplitude of ∼150 mV. The pulses amplitude remains constant at any data rate, which automatically scales the average output power with data rate. The active area of the TX occupies 0.0418 mm² (Fig. 13).

The TX performances are summarized in Table I and compared with state of the art transmitters for BAN. For a visual comparison of the TX performances, the total power consumption is plotted versus the data rate and compared with state of the art TXs in Figure 16. The power consumption of the surface-wave transmitter is at par or better than the stateof-the-art over the full range of data rates. When compared to UWB TX, this work shows a ∼5X improvement in energy efficiency at both high and low data rates for similar output power [START_REF] Streel | SleepTalker: A ULV 802.15. 4a IR-UWB transmitter SoC in 28nm FDSOI achieving 14 pJ/b at 27 Mb/s with channel selection based on adaptive FBB and digitally programmable pulse shaping[END_REF] without suffering from the body shadowing effect. Compared to m-BCC [17] the TX achieves a 20% reduction in power consumption at an equal data rate (5 Mb/s) with similar output power. With respect to a c-BCC (EQS) TX [START_REF] Cho | A 79 pJ/b 80 Mb/s Full-Duplex Transceiver and a 42.5µW 100 kb/s Super-Regenerative Transceiver for Body Channel Communication[END_REF], this SW solution improves the energy efficiency by 11X and 1.2X, for the high and low data rates, respectively over the state-ofthe-art. In comparison with [START_REF] Chatterjee | A Context-aware Reconfigurable Transmitter with 2.24 pJ/bit, 802.15. 6 NB-HBC and 4.93 pJ/bit, 400.9 MHz MedRadio Modes with 33.6% Transmit Efficiency[END_REF], this work offers a wider range of operating data rate and a higher maximum achievable data rate with equivalent energy efficiency order. [START_REF] Maity | BodyWire: A 6.3-pJ/b 30-Mb/s-30-dB SIR-tolerant broadband interference-robust human body communication transceiver using time domain interference rejection[END_REF] also reports a 30 Mb/s transmitter with similar energy efficiency (∼3pJ/b) but does not take into account the power consumption of the clock generation, which can easily be predominant in such low power circuits. Similarly, [START_REF] Lee | A 16.6-pJ/b 150-Mb/s body channel communication transceiver with decision feedback equalization improving¿ 200% area efficiency[END_REF] reaches a very high data rate up to 150 Mb/s with 3.5 pJ/b energy efficiency but does not take into account the power consumption of the frequency synthesis. Finally, [START_REF] Jang | 4-Camera VGA-resolution capsule endoscope with 80Mb/s bodychannel communication transceiver and Sub-cm range capsule localization[END_REF] also reports a very high data rate up to 80 Mb/s but at the cost of a 22X higher power consumption. The key techniques to reach state-of-the-art energy efficiency are: 1) ultra-low voltage operation enabled by bodybiasing 2) pulse-based communication for high duty-cycling 3) massively low-frequency operated architecture to reduce dynamic power consumption.

VI. ON-BODY DEMONSTRATION

Finally, as a proof of concept, a demonstration of the signal propagation on the human body is shown in Figure 17. On-body measurements are challenging for c-BCC because the ground planes must be considered carefully [START_REF] Cho | The human body characteristics as a signal transmission medium for intrabody communication[END_REF]. The transmitter is battery powered to avoid earth-grounded supply but the PCB-ground remains large and the amplifier at the RX side has a earth-grounded power supply. Although using SW as the dominant propagation mechanism limits the impact of the return path by nature, it should still be noted that such a setup is not fully representative of a communication between on-battery wearable devices with small ground planes. The signal propagation is demonstrated in two scenarios. The first scenario (in red in Figure 17) demonstrates a communication along the arm where the two electrodes are in line-of-sight with a distance up to 50 cm. In the second scenario (in blue in Figure 17), one electrode is placed on the pectoral while the second is placed on the shoulder blade. In this scenario, the electrodes are non-line-of-sight and spaced from each other at a distance of 30 cm (following the path along the body). This second scenario helps to demonstrate the capabilities of body coupled communication when undergoing the body shadowing effect. A 4 cm² electrode (similar to the one used in [START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF]) is wired to the output of the batterypowered TX and attached to the body. As a receiver, a TRF37D73EVM amplifier from Texas Instruments, offering a 20 dB gain, is attached to a second electrode with the same dimensions. The signal is then observed and recorded on a digital oscilloscope from Keysight (DSO91204A). In Figure 18 post-measurement signal processing is applied on 18. Signal processing on the measured received signal for the pectoral to shoulder scenario (blue in Figure 17).

the measured received signal. After applying a digital bandpass filter and envelope detection, the data can be easily retrieved. Furthermore, it is notable that when the electrodes are not attached to the body (air propagation) the signal is merely observable over a few centimeters. This demonstrates that the mechanism of propagation is truly body-coupled and not because of the electrodes acting as antennas with over-theair propagation. A video of the full demonstration is available at [START_REF] Tochou | RFIC Industry Showcase -IMS 2021[END_REF]. VII. CONCLUSION A 0.5 V 28 nm FD-SOI CMOS ultra-low voltage transmitter with flexible data rate (100 kb/s to 27 Mb/s) and using capacitive body-coupled communication with surface wave propagation is presented. The highly duty-cycled transmitter with pulse-based communication uses a low frequency, bodybias controlled oscillator and a switched-mode power amplifier with embedded frequency multiplier, Gaussian pulse shaping, and body-bias efficiency optimization. When compared to body-coupled transmitter state of the art, the proposed TX solution shows better flexibility as addressing a wider operation data rate, while staying at par or better in terms of energy efficiency. Moreover, the surface wave body-coupled link has been successfully demonstrated on-body using an offthe-shelf receiver. Future work will focus on the design of 1) a fully integrated solution with on-chip body-bias generation and periodic calibration, 2) the design of a receiver to demonstrate a full data transfer on-body, 3) electrode design with emerging flexible electronics and proper impedance matching, 4) and implementation of the MAC protocol [START_REF] Solt | Energy Efficient Heartbeat-Based MAC Protocol for WBAN Employing Body Coupled Communication[END_REF] for a full demonstration of the HI network [START_REF] Rabaey | Architecting the human intranet[END_REF].

Fig. 2 .

 2 Fig. 2. Contribution of each mechanism as a proportion of the total electric field for 21 MHz (dotted lines) and 450 MHz (solid lines).

Fig. 3 .

 3 Fig.3. Channel model of c-BCC SW[START_REF] Benarrouch | Capacitive Body-Coupled Communication in the 400-500 MHz Frequency Band[END_REF] compared to c-BCC EQS[START_REF] Bae | The signal transmission mechanism on the surface of human body for body channel communication[END_REF] and around-body UWB[START_REF] Fort | Ultra wide-band body area channel model[END_REF] (Note: The path loss models are normalized at 5 cm distance from the antenna/electrode as the insertion loss depends on the antenna/electrode impedance matching).

Fig. 4 .

 4 Fig. 4. Maximum pulse repetition frequency (i.e. maximum data rate) versus bandwidth trade-off.

Fig. 5 .

 5 Fig. 5. Proposed architecture of the c-BCC SW TX.

Fig. 6 .

 6 Fig. 6. (a) FD-SOI cross section and body-bias range (b) LVT NMOS/PMOS threshold voltage versus body-bias voltage.

Fig. 7 .

 7 Fig. 7. 90 MHz Pseudo-differential Ring-Oscillator (PD-RO) schematic.

Fig. 8 .

 8 Fig. 8. (a) Measured PD-RO Frequency vs body-bias voltage for 8 devices (b) PD-RO phase noise measurement (the plateau from 1 kHz to 100 kHz is due to the internal PLL of the measurement equipment locking).

Fig. 9 .

 9 Fig. 9. (a) Schematic of the EC-SCPA (b) Schematic of the digital pulse shaper (c) Timing diagram of the EC (d) Gaussian pulse envelop generated by the pulse shaper.

Fig. 10 .Fig. 11 .

 1011 Fig. 10. Model of the peak system efficiency (SE) of a SCPA at 450 MHz for different body-biasing values (Switches Area for an optimal load of 50 Ω).

Fig. 12 .Fig. 13 .

 1213 Fig. 12. Impact of pulse shaping waveform on the frequency response.

♯

  Note: For a 50 Ω load * Note: Not including frequency synthesis power consumption

Fig. 14 .

 14 Fig. 14. (a) Average output power and System Efficiency versus body-biasing voltage for different data rate (Lines represent the average measured values and the shaded curves represent the boundaries of minimum of maximum measured values on several devices) (b) Power Breakdown of the TX at VBB P A = +/-2.2 V versus data rate (error bars represent the boundaries of minimum of maximum measured values).

Fig. 15 .Fig. 16 .

 1516 Fig. 15. (a) Timing response of the TX at 27 Mb/s. (b) Spectrum response at 27 Mb/s. (c) Frequency response for different body bias voltage on the ring oscillator at 100 kb/s.

Fig. 17 .

 17 Fig. 17. (a) Set-up for on-body demonstration (b) Scenarios for on-body demonstration (c) Picture of the body-coupled demonstration on the arm.

  Andreia Cathelin (Senior Member, IEEE) started electrical engineering studies at the Polytechnic Institute of Bucarest, Romania and graduated with MS from the Institut Supérieur d'Electronique du Nord (ISEN), Lille, France in 1994. In 1998 and 2013 respectively, she received PhD and "habilitation à diriger des recherches" (French highest academic degree) from the Université de Lille 1, France. Since 1998, she has been with STMicroelectronics, Crolles, France, now Technology R&D Fellow. Her focus areas are in the design of RF/mmW/THz and ultra-low-power circuits and systems. She is the key design scientist in the promotion of all advanced CMOS technologies developed in the company. She is leading and driving research in advanced topics inside the company R&D program and through leadership cooperation with major universities around the world. She has also management activities as being in charge of the ST-CMP operation (the CMP is an independent organization offering small series foundry services for SME and research institutes). Andreia is very active in the IEEE community since more than 15 years, strongly implied with SSCS and its Adcom, the Executive Committee of VLSI Symposium and has been the TPC chair of ESSCIRC 2020 and 2021 in Grenoble. She has been for 10 years involved with ISSCC as RF subcommittee chair and then member of the Executive Committee. She is as well a founding member of the IEEE SSCS Women in Circuits group. Andreia has authored or co-authored 150+ technical papers and 14 book chapters, has co-edited the Springer book "The Fourth Terminal, Benefits of Body-Biasing Techniques for FDSOI Circuits and Systems" and has filed more than 25 patents. Andreia is a corecipient of the ISSCC 2012 Jan Van Vessem Award for Outstanding European Paper and of the ISSCC 2013 Jack Kilby Award for Outstanding Student Paper. She is as well the winner of the 2012 STMicroelectronics Technology Council Innovation Prize, for having introduced on the company's roadmap the integrated CMOS THz technology for imaging applications. Very recently, Andreia has been awarded an Honorary Doctorate from the University of Lund, Sweden, promotion of 2020.

TABLE I STATE

 I OF THE ART COMPARISON FOR BAN TX

		This Work
	Radio Technology	C-BCC (SW)	C-BCC (EQS)	C-BCC (EQS)	C-BCC (EQS)	C-BCC (EQS)	C-BCC (EQS)	C-BCC (EQS) IEEE 802.15.6	M-BCC	UWB IEEE 802.15.4a	BLE	BLE
	Process Technology	28nm FD-SOI		65nm	65nm	65nm	65nm	65nm	65nm	65nm	28nm FD-SOI	40nm	65nm
	Carrier Frequency [MHz]	350 -550	13.56	20 -60 140 -180	Baseband <100MHz	20 -120	20 -60 100 -180	Baseband <100MHz	22.27	40	3500 -4500	2400	2400
	Supply Voltage [V]	0.5		1.2	1	Not Reported	1.1	1	1	0.7		0.6	0.55	0.6	1.2
	Data rate [Mb/s]	0.1	27	0.1	80	100	2	80	30	1	10	5	0.11	27	1	1
	Modulation	OOK	OOK	BPSK	Decision Feedback Equalization	P-OFDM BPSK	QPSK BPSK	NRZ	OOK	OOK	BPM/BPSK	GFSK	GFSK
	TX Power Consumption [µW]	17	76	21	2600	350*	870	1700	93*	20.6	22.4	37	100	380	490	610
	TX Energy per bit [pJ/b]	170	2.8	210	32.5	3.5	435	22	3.1	20.6	2.24	7.2	950	14	490	610
	Output Power [dBm]	-33.8 ♯	-19.7 ♯	Not Reported	Not Reported	Not Reported	Not Reported	Not Reported	Not Reported	-24.8	-20	-19	-8.4
	TX System Efficiency [%]	2.44	14	Not Reported	Not Reported	Not Reported	Not Reported	Not Reported	Not Reported	17.8	2.6		2.6	23.6
	Area [mm²]	0.0418	0.1672	5.76	0.00348	0.54	1.3	0.02	0.117	0.0204	0.095	0.0166	0.494

G. De Streel [8] JSSC' 17 G. De Streel [8] JSSC' 17 UWB X. Chen [6] JSSC' 19 Y. Shi [7] ISSCC' 19 BLE J. Park [17] JSSC' 19 m-BCC S. Maity* [23] JSSC' 19 H. Cho [29] JSSC' 15 H. Cho [29] JSSC' 15 J. Lee* [24] VLSI' 17 J. Jang [26] ISSCC' 18 B. Chatterjee [27] RFIC' 20 B. Chatterjee [27] RFIC' 20 W. Saadeh [25] JSSC' 17 ACKNOWLEDGMENT The authors would like to thank Arno Thielens, Matthew G. Anderson, Ragnvald Nicolas Bernt and Matias Rietig for helpful discussions.

David Gaidioz (Member, IEEE) received the B.S. and M.S.Eng. degrees in electrical engineering from Bordeaux INP ENSEIRB-MATMECA, France, in 2014 and 2016, respectively. He received the Ph.D. degree in 2021 from the University of Bordeaux, France, under a CIFRE program with STMicroelectronics. He is currently a R&D engineer at STMicroelectronics. His research interests include frequency synthesis and low power wireless communications for the IoT and RFIC design.

under a CIFRE program with STMicroelectronics