Included here are figures and other relevant data from the paper "A distributed theory for contactless interconnects at terahertz frequencies". Abstract: Here we test a multimodal model for distributed contactless interconnects by comparing it to 3D full-wave simulations. In comparison to 3D simulations, the model offers insight into how the interconnect works and reduces the computational cost of estimating the interconnect?s performance. We predict the performance of four distributed contactless interconnects and find good agreement between our multimodal model and 3D simulations up to 1 THz. All the interconnects have less than 1 dB insertion loss in their first pass bands, highlighting the opportunity offered by contactless interconnects.

This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).

Data published in paper "Microstrip and Grounded CPW Calibration Kit Comparison for On-Wafer Transistor Characterization from 220 GHz to 325 GHz"We investigated the effect of two uncertainty sources, probe placement error and capacitance per unit length variation, on transistor S-parameter measurements calibrated with two different mTRL calibration kits. We propagated these uncertainties onto common-emitter (CE) and common-base (CB) heterojunction-bipolar-transistor (HBT) measurements to show how the calibration kit selection affects the accuracy of the resulting S-parameter transistor measurements and calculated characterization metrics such as K factor and maximum available gain (MAG). The measured data are from Sparameters taken from a Vector Network Analyzer (VNA). We used WR3.4 extender heads connected to a VNA and measured S-parameters from 210 GHz to 325 GHz with a 500 MHz frequency step. The probes were landed manually for each of calibration standard measurements and transistor measurements with an approximate probe landing error of +/- 10 um. Each raw measurement was stored and corrected later in post-processing using the mTRL calibration algorithm in the Microwave Uncertainty Framework (MUF). In this dataset, we also included the capacitance per unit length from a commercial Electromagnetic (EM) solver of the two transmission line cross sections used in the calibration kits. We varied the geometric and material properties of the transmission lines to obtain the histograms.

We present a method for accurately determining the permittivity of dielectric materials in 3D integrated structures at broadband RF frequencies. With applications of microwave and millimeter-wave electronics on the rise, reliable methods for measuring the electrical properties of dielectrics used in integrated circuits are critical. We outline an on-wafer method for extracting the permittivity of a 3D multilayer glass structure from 100 MHz to 30 GHz using S-parameter measurements of different calibration chips. Our method can be used to inform better design of metrology for dielectric materials for 3D integrated circuit technologies.This is data associated with the manuscript "Characterizing the broadband RF permittivity of 3D-integrated layers in a glass wafer stack from 100 MHz to 30 GHz" for the 2024 International Microwave Symposium (IMS) in Washington, D.C. The manuscript is currently under review by ERB in the NPS system under PUB ID 957051 / N2024-0193

Included here are figures and other relevant data from the paper "Measurements of nonlinear polarization dynamics in the tens of GHz" to be published in Physical Review Applied.Abstract: Frequency-dependent linear permittivity measurements are commonplace in the literature, providing key insights into the structure of dielectric materials. These measurements describe a material's dynamic response to a small applied electric field. In contrast, nonlinear dielectric materials are widely used for their responses to large applied fields, including switching in ferroelectric materials, and field-tuning of the permittivity in paraelectric materials. These behaviors are described by nonlinear permittivity. Nonlinear permittivity measurements are fraught with technical challenges because of the complex electrical coupling between a sample and its environment. Here, we describe a technique for measuring the complex nonlinear permittivity that circumvents many of the difficulties associated with other approaches. We validate this technique by measuring a the nonlinear permittivity of a tunable Ba0.5Sr0.5TiO thin film up to 40 GHz and comparing our results with a phenomenological model. These measurements provide insight into the dynamics of nonlinear dielectric materials down to picosecond timescales.

This data is provided as a supplement to NIST Technical Note 2159 Laboratory Method for Recording AWS-3 LTE Waveforms available at https://doi.org/10.6028/NIST.TN.2159. In particular, the data provided here are a compressed version of all the IQ recordings discussed in the report, with diagnostic information. The data is structured as a compressed archive, Data.zip, for each experimental configuration and capture repeat, resulting in 112 compressed archives organized by directory structure. The compressed archive, Data.zip, contains six files: IQ.csv, configuration.csv, diagnostic.csv, UE_diagnostic_spectrogram.csv, IQ_spectrogram.csv and IQ_spectrogram.png. - IQ.csv Description: Measured IQ at a sampling rate of 61.44 MS / s Contents: I and Q as signed integers, relative units- configuration.csv Description: The experimental configuration under which the IQ recording was made. Corresponds to a row in Table 4.1: Test Configurations of the technote. Contents: Scheduler_Awareness, Scheduler_Allocation, PowerControl_PUSCH, PowerControl_PUCCH,Scheduler_RBMask, P0_PUSCH, P0_PUCCH, alpha, UTG_NumUEs, UTG_ULRate, UTG_TrafficType, UTG_UERSRP, DUT_UE_ULRate, DUT_UE_TrafficType, UT_UE2_ULRate, DUT_UE2_TrafficType - diagnostic.csv Description: UE Diagnostic information with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), Total Tx Power (dBm), Resource Block Start, Number of Resource Blocks, MCS Index - UE_diagnostic_spectrogram.csv Description: The UE diagnostic report of power and resource block allocation organized into physical resource blocks with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), 0 ? max physical resource blocks in shared channel with a power in mw. - IQ_spectrogram.csv Description: IQ data organized into physical resource blocks with a time axis aligned to the diagnostic information. Contents: Elapsed time (ms), 0 ? 199 resource blocks with a relative power. - IQ_spectrogram.png Description: Plot of 140 ms of spectrogram, max relative power versus frequency and mean relative power, corresponds to appendix B of technote. Contents: Single image

This data is provided as a supplement to NIST Technical Note 2159 Laboratory Method for Recording AWS-3 LTE Waveforms available at https://doi.org/10.6028/NIST.TN.2159. In particular, the data provided here are a compressed version of all the IQ recordings discussed in the report, with diagnostic information. The data is structured as a compressed archive, Data.zip, for each experimental configuration and capture repeat, resulting in 112 compressed archives organized by directory structure. The compressed archive, Data.zip, contains six files: IQ.csv, configuration.csv, diagnostic.csv, UE_diagnostic_spectrogram.csv, IQ_spectrogram.csv and IQ_spectrogram.png. - IQ.csv Description: Measured IQ at a sampling rate of 61.44 MS / s Contents: I and Q as signed integers, relative units- configuration.csv Description: The experimental configuration under which the IQ recording was made. Corresponds to a row in Table 4.1: Test Configurations of the technote. Contents: Scheduler_Awareness, Scheduler_Allocation, PowerControl_PUSCH, PowerControl_PUCCH,Scheduler_RBMask, P0_PUSCH, P0_PUCCH, alpha, UTG_NumUEs, UTG_ULRate, UTG_TrafficType, UTG_UERSRP, DUT_UE_ULRate, DUT_UE_TrafficType, UT_UE2_ULRate, DUT_UE2_TrafficType - diagnostic.csv Description: UE Diagnostic information with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), Total Tx Power (dBm), Resource Block Start, Number of Resource Blocks, MCS Index - UE_diagnostic_spectrogram.csv Description: The UE diagnostic report of power and resource block allocation organized into physical resource blocks with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), 0 ? max physical resource blocks in shared channel with a power in mw. - IQ_spectrogram.csv Description: IQ data organized into physical resource blocks with a time axis aligned to the diagnostic information. Contents: Elapsed time (ms), 0 ? 199 resource blocks with a relative power. - IQ_spectrogram.png Description: Plot of 140 ms of spectrogram, max relative power versus frequency and mean relative power, corresponds to appendix B of technote. Contents: Single image

< 자율주행 시나리오 데이터셋: 멀티센서퓨전 > - 데이터셋 유형 - 멀티센서(카메라-LiDAR) 퓨전 Bounding Box-Panoptic Segmentation-Cuboid/Track ID 데이터셋 - 10초 동안 주행하며 수집한 자율주행 데이터를 5fps로 추출해 구축한 데이터셋 - 데이터셋 구성 1. 원천데이터_카메라 센서 데이터 1) 전방 FHD 카메라 이미지 데이터(.png): 50frames 2) 후방 FHD 카메라 이미지 데이터(.png): 50frames 3) 좌측방 FHD 카메라 이미지 데이터(.png): 50frames 4) 우측방 FHD 카메라 이미지 데이터(.png): 50frames 2. 원천데이터_LiDAR 센서 데이터 1) 전방위 128ch LiDAR 포인트 클라우드 데이터(.pcd): 50frames 2) 전방 장거리 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 3) 전방 중거리 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 4) 후방 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 5) 후방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 6) 좌측방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 7) 우측방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 8) 전방위 + 후방 사각지대 + 좌측방 사각지대 + 우측방 사각지대 데이터를 융합한 포인트 클라우드 데이터(.pcd): 50frames 3. 원천데이터_메타데이터(파일 속성 정보, 센서 파라미터 정보, 차량 절대위치 및 자세 정보, 차량 상태 정보, 주행 환경 및 상황 정보) 1) 전방 카메라 이미지 메타데이터(.json): 50frames 2) 후방 카메라 이미지 메타데이터(.json): 50frames 3) 좌측방 카메라 이미지 메타데이터(.json): 50frames 4) 우측방 카메라 이미지 메타데이터(.json): 50frames 5) 융합된 전방위 LiDAR 포인트 클라우드 메타데이터(.json): 50frames 4. 2D Bounding Box 어노테이션 데이터 1) 전방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 2) 후방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 3) 좌측방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 4) 우측방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 5. Panoptic Segmentation 어노테이션 데이터 1) 전방 카메라 이미지 Panoptic Segmentation 데이터(.json): 50frames 2) 전방 카메라 이미지 Panoptic Segmentation 마스크 이미지(.png): 50frames 3) 전방 카메라 이미지 Semantic Segmentation 마스크 이미지(.png): 50frames 4) 전방 카메라 이미지 Instance Segmentation 마스크 이미지(.png): 50frames 6. Cuboid(3D Bounding Box)/Track ID 어노테이션 데이터 1) 융합된 전방위 LiDAR 포인트 클라우드 Cuboid/Track ID 데이터(.json): 50frames 7. 데이터셋 정의서

This dataset is for the publication entitled "LABORATORY-BASED REFERENCE CHANNELS FOR MILLIMETER-WAVE WIRELESS DEVICE MEASUREMENTS." The dataset includes results from experiments in a simulated industrial wireless channel operated at 28 GHz. Results include synthetic-aperture beamforming data, and error-vector-magnitude information calculated from synthetic-aperture and directional-antenna measurements.

< 자율주행 시나리오 데이터셋: 멀티센서퓨전 > - 데이터셋 유형 - 멀티센서(카메라-LiDAR) 퓨전 Bounding Box-Panoptic Segmentation-Cuboid/Track ID 데이터셋 - 10초 동안 주행하며 수집한 자율주행 데이터를 5fps로 추출해 구축한 데이터셋 - 데이터셋 구성 1. 원천데이터_카메라 센서 데이터 1) 전방 FHD 카메라 이미지 데이터(.png): 50frames 2) 후방 FHD 카메라 이미지 데이터(.png): 50frames 3) 좌측방 FHD 카메라 이미지 데이터(.png): 50frames 4) 우측방 FHD 카메라 이미지 데이터(.png): 50frames 2. 원천데이터_LiDAR 센서 데이터 1) 전방위 128ch LiDAR 포인트 클라우드 데이터(.pcd): 50frames 2) 전방 장거리 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 3) 전방 중거리 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 4) 후방 MEMs LiDAR 포인트 클라우드 데이터(.pcd): 50frames 5) 후방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 6) 좌측방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 7) 우측방 사각지대 Hemispherical LiDAR 포인트 클라우드 데이터(.pcd): 50frames 8) 전방위 + 후방 사각지대 + 좌측방 사각지대 + 우측방 사각지대 데이터를 융합한 포인트 클라우드 데이터(.pcd): 50frames 3. 원천데이터_메타데이터(파일 속성 정보, 센서 파라미터 정보, 차량 절대위치 및 자세 정보, 차량 상태 정보, 주행 환경 및 상황 정보) 1) 전방 카메라 이미지 메타데이터(.json): 50frames 2) 후방 카메라 이미지 메타데이터(.json): 50frames 3) 좌측방 카메라 이미지 메타데이터(.json): 50frames 4) 우측방 카메라 이미지 메타데이터(.json): 50frames 5) 융합된 전방위 LiDAR 포인트 클라우드 메타데이터(.json): 50frames 4. 2D Bounding Box 어노테이션 데이터 1) 전방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 2) 후방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 3) 좌측방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 4) 우측방 카메라 이미지 2D Bounding Box 데이터(.json): 50frames 5. Panoptic Segmentation 어노테이션 데이터 1) 전방 카메라 이미지 Panoptic Segmentation 데이터(.json): 50frames 2) 전방 카메라 이미지 Panoptic Segmentation 마스크 이미지(.png): 50frames 3) 전방 카메라 이미지 Semantic Segmentation 마스크 이미지(.png): 50frames 4) 전방 카메라 이미지 Instance Segmentation 마스크 이미지(.png): 50frames 6. Cuboid(3D Bounding Box)/Track ID 어노테이션 데이터 1) 융합된 전방위 LiDAR 포인트 클라우드 Cuboid/Track ID 데이터(.json): 50frames 7. 데이터셋 정의서