B Duchemin{

O Hagolle{

B Mougenot{

I Benhadj{

R Hadria{

V Simonneaux{{

J Ezzahar{

J Hoedjes{

S Khabba{

M H Kharrou §

G Boulet{

G Dedieu{

S Er-Raki{

R Escadafal{

A Olioso

A G Chehbouni{

Agrometerological study of semi-arid areas: an experiment for analysing the potential of time series of FORMOSAT-2 images (Tensift-Marrakech plain)

Earth Observing Systems designed to provide both high spatial resolution (10 m) and high capacity of time revisit (a few days) offer strong opportunities for the management of agricultural water resources. The FORMOSAT-2 satellite is the first and only satellite with the ability to provide daily high-resolution images over a particular area with constant viewing angles. As part of the SudMed project, one of the first time series of FORMOSAT-2 images has been acquired over the semi-arid Tensift-Marrakech plain. Along with these acquisitions, an experimental data set has been collected to monitor land-cover/land-use, soil characteristics, vegetation dynamics and surface fluxes. This paper presents a first analysis of the potential of these data for agrometerological study of semiarid areas.

Introduction

Irrigated agriculture makes a major contribution to food security, producing nearly 40% of food and agricultural commodities on 17% of cultivated lands (FAO 2002) ;

. However, serious water shortages occur in semi-arid areas as existing resources reach full exploitation. The design of tools for providing with land-cover/land-use as well as regional estimates of water balance and crop yield is necessary to ensure a sustainable development of these areas. This is one of the objectives of the SudMed project which focus on the Tensift basin as a pilot region [START_REF] Chehbouni | Integrated modeling and remote sensing approach: toward a sustainable management of water resources in a semi-arid region[END_REF].

Effective management and monitoring of environmental resources require spatial data in order to integrate land-cover features as well as hydrological and vegetation parameters. Remote sensing may be the only feasible means of providing such information on a consistent space and time basis. Data acquired in the solar spectral domain have been the most intensively investigated in this context (e.g. [START_REF] Scotford | Applications of spectral reflectance techniques in Northern European cereal production: a review[END_REF]. Until now, they have been acquired either by high-resolution systems or by large field-of-view sensors. The former (Landsat-TM, SPOT-HRV …) provides data at a fine spatial resolution (10 m) but with moderate revisit capacities International Journal of Remote Sensing res151509.3d 28/4/08 08:39:55

The Charlesworth Group, Wakefield +44(0)1924 369598 -Rev 7.51n/W (Jan 20 2003) (around 15 days), so the possibility of temporal monitoring is limited. The latter (SPOT-VEGETATION, TERRA-MODIS …) observes the Earth on a daily basis but under a large range of viewing conditions (up to ¡55u off-nadir angles) and at a much coarser spatial resolution (250 m to 1 km). The use of low-spatial-resolution images is not trivial, since pixels generally include a mixture of different land classes.

Presently, the FORMOSAT-2 Taiwanese satellite is the only one able to acquire daily high-resolution images with constant viewing angles. In the perspective of the launch of others systems with similar characteristics such as GMES-Sentinel, RapidEye or Venms, one of the first time series of FORMOSAT-2 images has been acquired over the Tensift-Marrakech plain. In this context, the objective of this letter is twofold:

N to describe the main characteristics of FORMOSAT-2 images and of the ground data that have been acquired along with these acquisitions;

N to present a preliminary-mainly qualitative-analysis of the potential of these data for land-cover mapping and agricultural water management.

FORMOSAT-2/Tensift experiment

2.1 FORMOSAT-2/RSI Earth observing system FORMOSAT-2 has been launched by the National Space Organization of Taiwan (NSPO, http://www.nspo.org.tw/). It has been operational since May 2004 onto a Sun-synchronous orbit, with onboard the Remote Sensing Instrument (RSI). RSI provides high-spatial-resolution images (8 m in the multispectral mode for nadir viewing) in four narrow spectral bands ranging from 0.45 mm to 0.90 mm (blue, green, red, and near-infrared). Unlike other systems operating at high spatial resolution, FORMOSAT-2/RSI observes a particular area every day with the same viewing angle. However, it only surveys a part-about the half-of the Earth.

Space-time characteristics and processing of Formosat-2 images acquired over the Tensift/Marrakech Plain

The FORMOSAT-2/RSI images presented in this study have been collected from November 2005 to May 2006. The size of the scenes is 24 km along-track and 27 km cross-track, centred around 7u359 W631u409 N (figure 1). All images were acquired with an off-nadir angle of 18¡1u, viewing to the west across track. Given this viewing angle, the original pixel size is about 9 m in the cross-track direction and 8 m in the along-track direction. SPOT-Image has provided us with level 1A images. Images registration was performed using a three-step procedure: (1) absolute geolocation of the cloud-free image acquired December 2005 the 16th against a set of ground control points collected with GPS; (2) registration of this and other images using an autocorrelation algorithm; (3) resampling of data using Lambert North Morocco projection, with a sampling interval of 8 m.

The images were acquired with a nominal time step of 4 days, and about 50 images were collected during the 7-month period of interest (November to May). Twenty-six images were eliminated, most of them because they were contaminated by clouds, some others because ancillary data were unavailable for atmospheric correction. In this study, we kept only the images that were totally cloud-free, and the atmospheric correction was performed using the SMAC code [START_REF] Rahman | SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum[END_REF]. Atmospheric water vapour content and aerosol optical depth were collected using two CIMEL Sun photometers: the first was operational until June 2006 but located 50 km east from the region of interest, while the second was installed at the centre of the FORMOSAT-2 scene but only operational at the beginning of March. Some images could not be corrected for atmospheric effects (and thus rejected in this study) when there were clouds above the Sun photometers or in case of device failure. Finally, it should also be mentioned that some failures or conflicts in the programming occurred at the end of January, the beginning of February, and the beginning and end of April (see details in Duchemin et al. 2006a).

Due to the programming conflicts and high cloudiness, no data are available between mid-January and the beginning of March.

Two perspectives have to be mentioned for improving the processing of FORMOSAT-2 images. First, cloud detection and temporal compositing algorithms will be tested, so the remaining partially cloud-free images will allow the number of useful observations to be increased. Second, the high repetition of observations with constant viewing angles offers perspectives to enhance radiometric correction. Indeed, illumination conditions (Sun location) slowly and continuously vary over short periods (e.g. 10-15 days), during which the consistency of land surface may generally be supposed. We can thus expect a temporal stability of the top-ofatmosphere radiance collected by the sensor, except in case of variation of atmospheric components. This assumption is at the basis of the method we developed for estimating the aerosol content and performing the atmospheric correction. This method has been successfully tested on several FORMOSAT-2/RSI data sets including this one [START_REF] Hagolle | Atmospheric correction of multitemporal, monodirectional images. VENmS level 2 algorithms applied to FORMOSAT2 images[END_REF]).

Region of interest and experimental data

FORMOSAT-2/RSI images include the eastern part of the Tensift plain and the surrounding 'Jbilet' hills and foothills of High-Atlas mountains (figure 1). This To the south, High-Atlas mountains have considerable precipitation up to 600 mm year 21 , which supplies several large irrigated areas in the plain. Dominant crops are cereals (mostly wheat), fallows, and orchards (mainly olive trees); additional land classes include forages (mainly alfalfa) and vegetable crops (Simonneaux et al. in press). The main irrigated areas are managed by a regional public agency (ORMVAH) in charge of the distribution of dam water that is transported into regular irrigation units through concrete channels (figures 1 and 2). This network crosses over the traditional system, directly connected to High-Atlas oadis, which operates during flood events. Ground water is also used for irrigation by the mean of pumping stations. This organization results in a complex agricultural landscape with a high heterogeneity in field sizes and patterns, rather uniform within ORMVAH irrigated areas and more irregular on other areas (figure 1).

During the 2005/2006 agricultural season, an important experiment has been set up in the area observed by FORMOSAT-2/RSI. Two types of information were collected on numerous plots spread over the entire scene: surface reflectances, for testing the quality of atmospheric correction; and information on crop type, for evaluating land-cover mapping algorithm. As detailed in figure 2, additional data were collected on an ORMVAH irrigated area named R3 (white square in figure 1). This site was documented in Duchemin et al. The knowledge of land cover is a prerequisite for land surface monitoring and modelling studies. In this objective, remote sensing has been intensively investigated [START_REF] Rogan | Remote sensing technology for mapping and monitoring land-cover and land-use change[END_REF]. However, approaches based on a few images generally have limitations, since usually the spectral response of different classes may overlap, and the intra-class variability may be large. In this regard, the Haouz plain offers an interesting case study. Despite the land cover being rather simple, considerable disparity may exist for the same crop type. The characteristics of orchards can be very different depending on the age of trees and the spacing between stems. Furthermore, trees are sometimes cultivated together with another short crop. For cereals, the farmers do not go along the same technical itinerary, and there is considerable heterogeneity in the crop calendar as well as irrigation and fertilization schedules. The example of the fields monitored from 2002 to 2004 as part of the SudMed project showed that the sowing period ranged from mid-November to mid-January and that the quantity of Nitrogen fertiliser varied from 0 to 100 kg ha 21 (Hadria et al. in press). Finally, alfalfa is also a particular case: it is cultivated in small fields, and the harvest is rarely carried out for the entire field at the same time.

For these reasons, it is difficult to use ordinary classification methods based on statistical consistency between a set of spectral signatures and a thematic class (e.g. maximum likelihood).

In this context, the analysis of temporal patterns of simple surface characteristics, such as the greenness from vegetation indices, is probably more effective to map land cover in the Tensift-Marrakech area. A previous analysis of a set of eight Landsat-TM images acquired during the 2002-2003 agricultural season has shown the potential of this approach (Simonneaux et al. in press). From this time series, an NDVI profile was generated for each pixel, and the NDVI profiles were used to identify the main crop types of the Tensift-Marrakech region using a decision-tree algorithm. The resulting land-cover map was evaluated using ground data, and the confusion matrix computed gave a global accuracy up to 84%. However, the images used in this previous study were acquired on a monthly basis, and this work has allowed the distinction of only four broad land classes: bare soils, annual crops, trees, and annual crops mixed with trees. Thanks to the higher spatial and temporal resolution of FORMOSAT-2/RSI images, it is possible to gain both accuracy and refinement in the description of land cover; in particular we expect to discriminate additional sub-classes within annual crops by analysing the seasonality of growing and senescence of canopies.

A first example of NDVI time series derived from FORMOSAT-2/RSI on different crop types is given in figure 3. For each crop type, NDVI values were averaged over a group of two to eight plots juxtaposed and owned by the same farmer, ensuring the homogeneity of the agricultural practices; the average includes 1300-3500 pixels depending on the crop type, except for alfalfa for which only a small single plot of about 100 pixels was targeted. Figure 3 presents the NDVI seasonal courses for groups of plots representative of seven land classes split into two categories. The difference in NDVI levels between the three 'stable' targets (figure 3(a)) is consistent with surface features: the NDVI is always low (around 0.15) for bare soil and high (around 0.7) for alfalfa, which most of time totally covers the soil, while it displays intermediate values (around 0.4) for the young olive Second, NDVI seasonal patterns agree with ground observations and climatic features: the growing period was delayed for early wheat (sown 1 December) compared with late wheat (sown 2 January); for the fallow, the vegetation grows after the first significant rainfalls (end of December/beginning of January), and the senescence starts at the beginning of April after the drought observed in March (no rain this month), much earlier than for irrigated crops. Finally, it can be noticed that the senescence occurs nearly at the same time for the two wheat fields despite the differences between their sowing dates. The explanation for this is likely twofold: (1) plant emergence is delayed for the early sown wheat due to soil dryness, since there is only light rainfall and no irrigation before 24 December; (2) phenological stages are shortened at the end of the season, since temperatures know an important increase < (maximal air temperatures of up to 40uC were recorded at the end of March compared with 15-20uC at the beginning of January). From the analysis of the example displayed in figure 3, it appears realistic to separate at least seven land classes using a decision-tree approach with simple criteria applied on NDVI time series (min./max. values, amplitude and phases …). This is an improvement compared with the previous analysis by Simonneaux et al. (in press) based on the 2002-2003 Landsat-TM data set. This information will serve to benchmark different land-cover mapping algorithms applied at various spatial, temporal, or categorical scales, i.e. for various types and groups of land-cover categories [START_REF] Ju | On the choice of spatial and categorical scale in remote sensing land cover classification[END_REF]. In particular, it will be used as a reference to validate regional applications based on coarse spatial resolution data [START_REF] Benhadj | Land use in semi arid area derived from NDVI images at[END_REF].

Agricultural practices and water management

Agricultural water management and planning is a key issue for sustainable development of arid and semi-arid areas. There is a crucial need to develop tools for quantitative monitoring of water and vegetation resources at regional scale. The scientific community has shown increasing interest in methodologies based on agro-ecological process modelling and remote sensing [START_REF] Olioso | Future directions for advanced evapotranspiration modeling: assimilation of remote sensing data into crop simulation models and SVAT models[END_REF]. In this context, a daily survey with FORMOSAT-2/RSI would allow the description of the seasonality of soil-plant systems to be improved, therefore strengthening the results based on the assimilation of remote sensing observations into coupled crop/water balance models.

A more original opportunity is offered by FORMOSAT-2/RSI for a direct characterization of some aspects related to land-use. This is first illustrated from a time composite of three images acquired from 4 to 12 December (figure 4). Looking at the whole area in this figure, it can be seen that most pixels display grey tones. In these pixels, the land status is still nearly constant, with a more or less bright surface, over the 8-day period of interest. Indeed, this period corresponds to sowing of cereals, which are dominant in the area of interest, and the grey areas mainly include bare soils with no change in their status from 4 to 12 December. Some other fields display colours which indicate a steep variation of reflectances: decrease from 4 December to 8 December in red, decrease from 8 December to 12 December in yellow, increase from 4 December to 12 December in cyan to dark blue, or a peak around 8 December in green. These changes result from phenomena that induce a rapid change in surface status, among which we have identified ploughing, irrigation, topsoil drying, soil recovering by greenhouses, or harvest. Figure 5 details some of these effects from a time series of reflectances averaged for wheat fields on which technical itineraries have been recorded. There was only light rainfall at the end of November and no developed plant during this period. The reflectances displayed in figure 5 were normalized against those observed on bare soils (fallow area in figure 3). Furthermore, a steep variation of reflectance cannot be due to directional effects since: (1) viewing angles are constant throughout all the time series, (2) the viewing direction is far from the hot spot direction, and (3) Sun angles vary slowly with time. Given this, we believe that the temporal variations of reflectances are caused by local changes in the topsoil status. It can be seen from figure 5 that ploughing can be detected, since it results in a large decrease in reflectances due to the increase in soil roughness and shadowing. Thus, at the very beginning of the season, the level of reflectances appears as an indicator of the fact 5). Furthermore, a decrease in reflectances can be observed just before sowing, though it is less visible when deep ploughing has occurred. Additionally, a decrease in reflectance is also noticeable after an irrigation event due to the drastic increase in topsoil moisture. Indeed, water has a profound effect on soil spectral signature, decreasing the reflectance in all solar wavelengths. One way is therefore shown here to determine ploughing and irrigation dates at the beginning of the season, which is a critical input of evaporation and crop models.

This first investigation shows the potential of FORMOSAT-2/RSI images to directly derive characteristics about land use that are critical in crop modelling. This information may provide additional input in inversion scheme to better control crop and water-balance models. It will be particularly useful in a regional application, which generally suffers from the lack of spatially distributed data related to agricultural practices.

Figure 1 .

 1 Figure 1. FORMOSAT image acquired 22 March 2006 (R r near infrared; G r green; B r red). ORMVAH irrigated areas are depicted in green, with the main area of interest delimited by a white square. Most important irrigation channels and rivers are highlighted in cyan and blue, respectively.

  Figure 2. Details of the experimental set-up (type and/or spatial scale, land-cover type) collected at the centre of the R3 irrigated area. White lines highlight the irrigation network.

International

  Journal of Remote Sensing res151509.3d 28/4/08 08:40:10 The Charlesworth Group, Wakefield +44(0)1924 369598 -Rev 7.51n/W (Jan 20 2003)Agrometerological study of semi-arid areas 5 the vegetation fraction cover was estimated to about 30%; for alfalfa, one can see rather large NDVI variations of ¡0.2 around the average value, which result from the succession of cutting and regrowth periods. The differences in NDVI seasonal courses between annual plants (figure3(b)) also appear coherent with the vegetation dynamics. First, the seasonal amplitude is maximal for wheat crops (about 0.65), on which the surface changes from bare soils to an almost totally covering canopy; the amplitude is minimal on the fallow, though it is rather high (about 0.45), indicating the presence of 'natural' vegetation (wild oat and colza).

International

  Figure 3. NDVI time courses derived from FORMOSAT-2/RSI images. (a) Three cases of 'stable' surfaces: alfalfa ( N ), young olive trees ( * ) and bare soil (n). (b) Four cases of annual plants: early (e) and late (m) sown wheat, sugar beet (%), fallow (6).

Figure 4 .

 4 Figure 4. Time composite image of red reflectances acquired 4 December (red band), 8 December (green band), and 12 December (blue band). The top-right image displays the whole main area of interest (see figure 1), while the two other images correspond to successive zooms over the areas delimited with red squares.

  been ploughed at the end of the previous season to incorporate the residue in the soil (called deep ploughing in figure

Figure 5 .

 5 Figure 5. Time courses of green reflectances averaged on four wheat fields, with their various technical itineraries indicated by the labels. Rainfall is displayed with bars at top of the figure (secondary right axis). The reflectances were normalised against those observed on bare soils (fallow area in figure 3).

Acknowledgements

This research was part of the SudMed project. In addition to IRD, this work was funded by the European Commission: IRRIMED (FP5) and PLEIADeS (FP6) projects. Additionnal support was provided by the French 'Programme National de Te ´le ´de ´tection Spatiale' (PNTS) and the French Space Agency (CNES). We acknowledge NSPO, SPOT-Image, and CNES for the delivery and the processing of FORMOSAT-2 images. We are indebted to the staff and the director of ORMVAH for their assistance during this experiment. We are grateful to the AERONET network for their help in the acquisition of CIMEL Sun-photometer data.


Chandan Yadav

Marco Cabbia

Sebastien Fregonese

Marina Deng

Magali De Matos

Thomas Zimmer

Guideline for test-structures placement for on-Wafer calibration in sub-THz Si device characterization

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Guideline for test-structures placement for on-Wafer calibration in sub-THz Si device characterization Abstract-In this paper, we present a guideline to optimize the layout floorplan by minimizing the impact of on-wafer neighbouring structures in very high frequency (1 GHz to 220 GHz) on-wafer measurements of Si electronic devices. To present the guideline, a 3D electromagnetic (EM) simulation is carried out extensively using a realistic EM model of a commercial RF probe. First layout design dependent factors influencing the DUT characteristics are identified which are 1) way of positioning of the on-wafer structures w.r.t. DUT (e.g. in line or checkerboard pattern) and 2) the spacing between on-wafer structures and the DUT. Afterwards, a guideline to reduce the influence of on-wafer neighbours on the DUT characteristics is presented. The optimization of the layout floorplan with minimal on-wafer neighbours prior to their fabrication also permits to reduce the costs with respect to occupied Si area.

Index Terms-On-wafer TRL calibration, test-structures, EM simulation, RF probes, Spatial placement, sub-THz.

I. INTRODUCTION

O F late, millimeter wave (mmWave) and sub-millimeter wave (sub-mmWave) have received attention for many applications e.g. high speed 5G communication, automotive radar, biomedical applications etc. [START_REF] Sengupta | Terahertz Integrated Electronic and hybrid Electronic-Photonic Systems[END_REF], [START_REF] Hillger | Terahertz Imaging and Sensing Applications With Silicon-Based Technologies[END_REF]. For these high frequency (HF) applications, silicon (Si) and III-V technologies based devices and circuits are being explored and the on-wafer measurement is widely adopted in their characterization [START_REF] Chevalier | Si/SiGe:C and InP/GaAsSb Heterojunction Bipolar Transistors for THz Applications[END_REF].

A compact model of an electronic device has become integral part in the integrated circuit and system designs. Accurate measurement data is an essential requirement for reliable compact model parameter extraction of electronic devices but at high frequencies accurate measurements with band-to-band continuity is a challenging task [START_REF] Sia | Minimizing discontinuities in wafer-level sub-THz measurements up to 750 GHz for device modelling applications[END_REF], [START_REF] Fregonese | On-Wafer Characterization of Silicon Transistors Up To 500 GHz and Analysis of Measurement Discontinuities Between the Frequency Bands[END_REF]. For accurate HF-measurement of the electronic devices, layout design of test-structures plays a very important role [START_REF] Kolding | Shield-based microwave on-wafer device measurements[END_REF], [START_REF] Eggebert | On the Accurate Measurement and Calibration of S-parameters for Milimeter Wavelenths and Beyond[END_REF]. In addition, different factors e.g. probe design, pad design, substrate material, calibration techniques etc. can influence the measured S-parameters [START_REF] Andrei | Coupling On-wafer Measurement Errors and their Impact on Calibration and De-embedding up to 110 GHz for CMOS millimeter wave Characterizations[END_REF], [START_REF] Fregonese | Comparision of On-Wafer TRL Calibration to ISS SOLT Calibration with Open-Short De-embedding up to 500 GHz[END_REF]. On a high packing density silicon wafer, calibration kit structures as well as the active and passive devices are fabricated in very close vicinity to each other. Due to high packing density, influence of the onwafer neighbouring environment on the on-wafer S-parameter measurement of a DUT cannot be ignored. Since, Si wafer area occupied by a device or circuit is directly related to cost. Therefore, optimizing a layout floorplan of calibration kit structures for accurate on-wafer S-parameter measurements of a DUT by optimal placement of on-wafer neighbours can potentially reduce the occupied wafer area.

II. DESCRIPTION OF SPATIAL POSITION OF STRUCTURES

The transistor-open shown in Fig. 1 is used as a DUT and its characteristics are studied under different on-wafer neighbouring environments shown in Fig. 2. The on-wafer neighbouring structures placement around a DUT (inside a dashed box) in Fig. 2 are divided into two categories: 1) neighbours placement either parallel to the DUT (Fig. 2a) or behind the RF probes (Fig. 2b and Fig. 2c, here in a diagonal checkerboard pattern). Note that due to page limit, only few combinations of spatial placement of structures placements (but the most relevant) around a DUT are discussed in this work while many more other combinations are possible. All the structures in this study have 8-metal layers in the backend-of-line (see Fig. 1c) and are designed for fabrication on Si similar to described by authors in [START_REF] Deng | Design of Silicon On-Wafer sub-THz Calibration Kit[END_REF]. Picoprobe probes with 100 µm and 50 µm pitch are used in the measurement between 1 -110 GHz and 140 -220 GHz but only the port-2 Picoprobe used in measurement is modelled in HFSS (see Fig. 3). Which is used to perform the EM simulation of the on-wafer TRL calibration test-structures as well as DUT including the probe. Since the work presented in [START_REF] Yadav | Importance and Requirement of Frequency Band Specific RF Probes EM Models in Sub-THz and THz Measurements up to 500 GHz[END_REF] shows that simulation results using RF probe model corresponding to

III. RESULTS AND DISCUSSION

A. Improving on-wafer Measurement of a Discrete DUT First, the on-wafer TRL calibration is performed using the measured raw S-parameters of the thru, reflect and lines fabricated on the Si-BiCMOS55 technology of STMicroelectronics [START_REF] Chevalier | A 55 nm triple gate oxide 9 metal layers SiGe BiCMOS technology featuring 320 GHz f T / 370 GHz f MAX HBT and high-Q millimeter-wave passives[END_REF] and applied on the transistor-open (DUT), see Fig. 1c. The TRL calibrated on-wafer S-parameter of transistor-open are shown in Fig. 4 with the symbols. In the on Si wafer fabricated structures, each structure is surrounded by a 10 µm wide oxide ring to electrically isolate each structure. Using the procedure adopted in [START_REF] Yadav | Importance and Requirement of Frequency Band Specific RF Probes EM Models in Sub-THz and THz Measurements up to 500 GHz[END_REF] and RF probe model shown in Fig. 3 show that the presence of an oxide ring around structures increases the coupling with the substrate and between port-1 and port-2. This phenomenon has already been observed and investigated on III-V technologies [START_REF] Phung | Impact of Parasitic Coupling on multiline TRL Calibration[END_REF]. The reduced coupling in Fig. 4d compared to Fig. 4c explain this improvement in S-parameter behavior in Fig. 4a andb.

B. Impact of Spatial Placement of on-Wafer Neighbours

Further, to analyse the impact of the spatial placement of the on-wafer neighbours around a DUT, structures with ground shield i.e. without oxide ring are used. If on-wafer neighbours do not have any impact on the S-parameter of a DUT, then the measured S-parameter or the from S-parameter derived characteristics of a DUT by including or excluding the on-wafer neighbours should be the exactly same. For the on-wafer placement of structures in Fig. 2a and Fig. 2b, the % variations in C 11 of the DUT are shown in Fig. 5a and Fig. 5b, respectively. Both the placement designs show non-zero variations hence highlights that presence of an on-wafer structure around the DUT can have some influence on the DUT characteristics. A non-zero percentage variation in C 11 can be attributed to variations in electric field distribution of a DUT due to parasitic couplings after including the onwafer structures around it (compare Fig. 4d with Fig. 5c and Fig. 5d). Since, variations in C 11 of DUTs with on-wafer structures in Fig. 2a and Fig. 2b are different for the same value of the "d" or "l", this indicates that with change in spatial placement of the on-wafer structures, their influences on DUT characteristics varies. These variations are associated with change in parasitic couplings due to the spatial placement of structures (compare Fig. 5c and Fig. 5d with 6a and Fig. 6). With stronger variations in C 11 corresponding to placements in Fig. 2b indicates that an on-wafer structure just below the RF probe signal trace and very close to the DUT (e.g. see variation with "d") should be avoided.

In Fig. 5, the % variation in C 11 w.r.t. frequency is not constant for a given value of 'd' or 'l. This frequency dependent variation in DUT characteristics is probably due to the change in the signal wavelength which can alter the interaction / coupling of a DUT with the same neighbours. The frequency dependent variation in parasitic coupling of the DUT with neighbours can be observed by comparing the electric field distribution for the same "d" in Fig. 5c and Fig. 6c or Fig. 5d and Fig. 6d at 120 GHz and 200 GHz, respectively.

From the aforementioned observation we deduce that the structure placement below the RF probe requires a careful strategy. Thus neighbours' placement shown in Fig. 2c is analyzed further. In this diagonal checkerboard pattern design, the structures are placed at a distance l/2 from the center of the signal trace of the RF probes. In Fig. 7a IV. CONCLUSION It is observed that the influence of the spatial placement of an on-wafer structure around a DUT varies with change in their location and placement distance w.r.t. the DUT. A greater attention is required while placing a structure just below the signal trace of a RF probe. It has been shown that when using a diagonal checkerboard pattern, the influence of an on-wafer structures on the DUT characteristics becomes negligible and further increase of the distance would increase the wafer area and thus the costs.

Fig. 1 .Fig. 2 .

 12 Fig. 1. (a) 2D schematic of transistor-open used as a DUT in this study where G and S refers to the ground and signal, respectively. Panel (b) shows capacitances associated with port-1, port-2 and between port-1 and port-2 of transistor-open. In panel (c), 8 metal lines in back-end-of-lines are shown using a cross sectional view of the transistor-open.l l

Fig. 3 .

 3 Fig. 3. (a) Image of the Picoprobe probe used at port-2 during measurement in 140GHz -220 GHz frequency band and (b) 3-D sketch of the probe modeled in HFSS analogous to probe shown in panel (a).

  Fig. 4. S-parameters (S 22 and S 21 ) of transistor-open with and without oxide ring around the DUT and calibration kit structures. The measurement data is corresponding to the fabricated structures having oxide ring around them. Due to the oxide ring (indicated in panel c by white color), the reflection S-parameter goes above 0 dB due to increased coupling with the substrate as shown in panel (c) at 120 GHz while replacing the oxide ring with a metal leads to expected value of S 22 below 0 dB due to reduced parasitic coupling as shown in panel (d).

Fig. 5 .Fig. 6 .

 56 Fig. 5. Panel (a) and (b) shows percentage variation in Port-1 capacitance C 11 of the transistor-open (DUT) with neighbours (shown in inset and Fig. 2) in reference to DUT without neighbour shown in Fig. 1a. A non-zero % variation shows influence of the on-wafer structures on DUT. Panel (c) and (d) shows electric field distribution in transistor-open in presence of on-wafer structures of design Fig. 2a at 120 GHz for two values of "d". (a) (b)

  Here we focus on the transistor-open capacitances, which are more relevant for device modeling purposes. The percentage variations in port-1 capacitance of the transistor-open (C 11 ) are calculated as 100 ×(C 11,N -C 11,ref ) /C 11,ref in presence of the on-wafer

Fig. 7 .

 7 Fig. 7. The percentage variation in C 11 of transistor-open (DUT) with neighbours of design in Fig. 2c in respect to DUT without neighbours, (a) l = 20µm and (b) l = 120µm. In the panel (a), the legend entry for 'd' is the same as shown in (b). Panel (c) and (d) shows electric field coupling at 120 GHz. neighbours where C 11,ref and C 11,N are capacitance excluding (i.e. reference data) and including the on-wafer neighbours, respectively. A zero and non-zero percentage variation in C 11 of the DUT would indicate no impact (i.e. ideal placement) and impact of the spatial placement of structures around a DUT, respectively. Further analysis is presented for different types of spatial placement of the on-wafer structures around a DUT (transistor-open) shown in Fig. 2.For the on-wafer placement of structures in Fig.2aand Fig.2b, the % variations in C 11 of the DUT are shown in Fig.5aand Fig.5b, respectively. Both the placement designs show non-zero variations hence highlights that presence of an on-wafer structure around the DUT can have some influence on the DUT characteristics. A non-zero percentage variation in C 11 can be attributed to variations in electric field distribution of a DUT due to parasitic couplings after including the onwafer structures around it (compare Fig.4dwith Fig.5cand Fig.5d). Since, variations in C 11 of DUTs with on-wafer structures in Fig.2aand Fig.2bare different for the same value of the "d" or "l", this indicates that with change in spatial placement of the on-wafer structures, their influences on DUT characteristics varies. These variations are associated with change in parasitic couplings due to the spatial placement of structures (compare Fig.5cand Fig.5dwith 6a and Fig.6). With stronger variations in C 11 corresponding to placements in Fig.2bindicates that an on-wafer structure just below the RF probe signal trace and very close to the DUT (e.g. see variation with "d") should be avoided.In Fig.5, the % variation in C 11 w.r.t. frequency is not constant for a given value of 'd' or 'l. This frequency dependent variation in DUT characteristics is probably due to the change in the signal wavelength which can alter the interaction / coupling of a DUT with the same neighbours. The frequency dependent variation in parasitic coupling of the DUT with neighbours can be observed by comparing the electric field distribution for the same "d" in Fig.5cand Fig.6cor Fig.5dand Fig.6dat 120 GHz and 200 GHz, respectively.From the aforementioned observation we deduce that the structure placement below the RF probe requires a careful strategy. Thus neighbours' placement shown in Fig.2cis analyzed further. In this diagonal checkerboard pattern design, the structures are placed at a distance l/2 from the center of

  and Fig. 7b, the variation in the C 11 behaviour of the transistor-open is shown for two values of 'l' i.e. 20 µm and 100 µm. It can be easily noticed that in Fig. 7, for l = 20 µm the variation in the C 11 is more pronounced because some part of the neighbouring structures are either lying very close to or below the signal trace. The same conclusions can be drawn qualitatively from the electric field distribution of the DUT with on-wafer structures for l = 20 µm and 100 µm shown in Fig. 7c and Fig. 7d. Note that these observed variations may vary quantitatively with change in test-structures design or RF probe design.