75 research outputs found

    FPGA๋ฅผ ์ด์šฉํ•œ ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ ๊ณ ์ง‘์  PET ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์žฅ์น˜

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜๊ณผ๋Œ€ํ•™ ์˜๊ณผํ•™๊ณผ,2019. 8. ์ด์žฌ์„ฑ.Positron emission tomography (PET) is a widely used functional imaging device for diagnosing cancer and neurodegenerative diseases. PET instrumentation studies focus on improving both spatial resolution and sensitivity to improve the lesion detectability while reducing radiation exposure to patients. The silicon photomultiplier (SiPM) is a photosensor suitable for high-performance PET scanners owing to its compact size and fast response. However, the SiPM-based PET scanners require a large number of readout channels owing to a high level of granularity. For example, the typical whole-body PET scanners require more than 40,000 SiPM channels. Therefore, the highly integrated data acquisition (DAQ) system that can digitize a large number of SiPM signal with preserving its fast temporal response is required to develop the high-performance SiPM-based PET scanners. Time-based signal digitization is a promising method to develop highly integrated DAQ systems owing to its simple circuitry and fast temporal response. In this thesis, studies on developing highly integrated DAQ systems using a field-programmable gate array (FPGA) were presented. Firstly, a 10-ps time-to-digital converter (TDC) implemented within the FPGA was developed. The FPGA-TDCs suffer from the non-linearity, because FPGAs are not originally designed to implement TDC. We proposed the dual-phase sampling architecture considering the FPGA clock distribution network to mitigate the TDC non-linearity. In addition, we developed the on-the-fly calibrator that compensated the innate bin width variations without introducing the dead time. Secondly, the time-based SiPM multiplexing and readout method was developed using the principle of the global positioning system (GPS). The signal traces connecting every SiPM to four timing channels were used to encode the position information. The position information was obtained using the innate transit time differences measured by four FPGA-TDCs. In addition, the minimal signal distortion by multiplexing circuit allowed to use a time-over-threshold (ToT) method for energy measurement after multiplexing. Thirdly, we proposed a new FPGA-only digitizer. The programmable FPGA input/output (I/O) port was configured with stub-series terminated logic (SSTL) input receiver, and each FPGA I/O port functioned as a high-performance voltage comparator with a fast temporal response. We demonstrated that the FPGA can be used as a high-performance DAQ system by directly digitizing the time-of-flight (TOF) PET detector signals using the FPGA without any front-end electronics. Lastly, we developed comparator-less charge-to-time converter (QTC) DAQ systems to collect data from a prototype high-resolution brain PET scanner. The energy channel consisted of a QTC combined with the SSTL input receiver of the FPGA. The timing channel was a TDC implemented within the same FPGA. The detailed structure of brain phantom was well-resolved using the developed high-resolution brain PET scanner and the highly-integrated time-based DAQ systems.์–‘์ „์ž๋ฐฉ์ถœ๋‹จ์ธต์ดฌ์˜ (Positron Emission Tomography; PET) ์žฅ์น˜๋Š” ์•”๊ณผ ์‹ ๊ฒฝํ‡ดํ–‰์„ฑ ์งˆํ™˜์„ ์˜์ƒํ™”ํ•˜๋Š” ๋ฐ ๋„๋ฆฌ ์“ฐ์ด๋Š” ๊ธฐ๋Šฅ ์˜์ƒ์žฅ์น˜์ด๋‹ค. ์ตœ๊ทผ PET ์Šค์บ๋„ˆ ์—ฐ๊ตฌ๋Š” ๊ณต๊ฐ„ ๋ถ„ํ•ด๋Šฅ๊ณผ ์žฅ๋น„ ๋ฏผ๊ฐ๋„๋ฅผ ๋†’์—ฌ ๋ณ‘๋ณ€์˜ ์ง„๋‹จ์„ ์‰ฝ๊ฒŒ ํ•˜๋ฉด์„œ ํ™˜์ž์˜ ๋ฐฉ์‚ฌ์„  ํ”ผํญ์„ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์‹ค๋ฆฌ์ฝ˜ ๊ด€์ฆ๋ฐฐ๊ธฐ (silicon photomultiplier; SiPM)์€ ํฌ๊ธฐ๊ฐ€ ์ž‘๊ณ  ๋ฐ˜์‘์†๋„๊ฐ€ ๋น ๋ฅด๊ธฐ ๋•Œ๋ฌธ์— ๊ณ ์„ฑ๋Šฅ PET ์Šค์บ๋„ˆ์— ์ ํ•ฉํ•œ ๊ด‘๊ฒ€์ถœ์†Œ์ž์ด๋‹ค. ํ•˜์ง€๋งŒ SiPM ๊ธฐ๋ฐ˜ PET ์Šค์บ๋„ˆ๋Š” ๊ฐœ๋ณ„ SiPM์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘๊ธฐ ๋•Œ๋ฌธ์— ์ˆ˜๋งŽ์€ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์ฑ„๋„์ด ํ•„์š”ํ•˜๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ „์‹  PET ์Šค์บ๋„ˆ๋ฅผ SiPM์œผ๋กœ ๊ตฌ์„ฑํ•  ๊ฒฝ์šฐ 40,000๊ฐœ ์ด์ƒ์˜ SiPM ์†Œ์ž๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ, SiPM์˜ ์„ฑ๋Šฅ์„ ์œ ์ง€ํ•˜๋ฉด์„œ ๋‹ค์ฑ„๋„ ์‹ ํ˜ธ ๋””์ง€ํ„ธํ™”๊ฐ€ ๊ฐ€๋Šฅํ•œ ๊ณ ์ง‘์  ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์žฅ์น˜ (data acquisition; DAQ)๊ฐ€ ๊ณ ์„ฑ๋Šฅ SiPM PET ์Šค์บ๋„ˆ ๊ฐœ๋ฐœ์— ํ•„์š”ํ•˜๋‹ค. ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ ์‹ ํ˜ธ ๋””์ง€ํ„ธ ๋ฐฉ๋ฒ•์€ ๋‹จ์ˆœํ•œ ํšŒ๋กœ์™€ ๋น ๋ฅธ ๋ฐ˜์‘์†๋„ ๋•๋ถ„์— ๊ณ ์ง‘์  DAQ ์‹œ์Šคํ…œ์„ ๊ตฌํ˜„ํ•˜๋Š” ์œ ๋งํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ํ”„๋กœ๊ทธ๋žจ ๊ฐ€๋Šฅ ๊ฒŒ์ดํŠธ ๋ฐฐ์—ด (field-programmable gate array; FPGA)์„ ์ด์šฉํ•˜์—ฌ ๊ณ ์ง‘์  DAQ ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ•˜๋Š” ์—ฐ๊ตฌ๋‚ด์šฉ์„ ๋‹ค๋ฃฌ๋‹ค. ์ฒซ์งธ๋กœ, 10 ps ์˜ ๋ถ„ํ•ด๋Šฅ์„ ๊ฐ–๋Š” FPGA ๊ธฐ๋ฐ˜ ์‹œ๊ฐ„-๋””์ง€ํ„ธ ๋ณ€ํ™˜๊ธฐ (time-to-digital converter; TDC)๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. FPGA๋Š” TDC ๊ตฌํ˜„์„ ์œ„ํ•œ ์ง‘์ ์†Œ์ž๊ฐ€ ์•„๋‹ˆ๋ฏ€๋กœ FPGA์— ๊ตฌํ˜„๋œ TDC๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ๋น„์„ ํ˜•์„ฑ ๋ฌธ์ œ๋ฅผ ๊ฐ€์ง„๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋น„์„ ํ˜•์„ฑ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•˜๋Š” FPGA์˜ ํด๋ฝ ์‹ ํ˜ธ ๋ถ„๋ฐฐ ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ด์ค‘ ์œ„์ƒ ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ, FPGA TDC ๊ณ ์œ ์˜ ๋ถˆ๊ท ์ผํ•œ ๋ถ„ํ•ด๋Šฅ์„ ์ธก์ •ํ•˜๊ณ  ๋ณด์ƒํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ ๋ณด์ •๊ธฐ์ˆ ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋‘˜์งธ๋กœ, GPS ์›๋ฆฌ๋ฅผ ์‚ฌ์šฉํ•œ ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ ์‹ ํ˜ธ ๋ถ€ํ˜ธํ™” (multiplexing) ๋ฐ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ถ€ํ˜ธํ™” ํšŒ๋กœ๋Š” SiPM์„ ๋„ค ๊ฐœ์˜ ์‹œ๊ฐ„ ์ˆ˜์ง‘ ์ฑ„๋„๋กœ ์—ฐ๊ฒฐํ•œ ๋„์„ ์œผ๋กœ ๊ตฌ์„ฑ๋˜๊ณ  ์œ„์น˜์ •๋ณด๋Š” ๊ฐ SiPM์œผ๋กœ๋ถ€ํ„ฐ ๋„ค ๊ฐœ์˜ ์‹œ๊ฐ„ ์ˆ˜์ง‘ ์ฑ„๋„๊นŒ์ง€์˜ ๊ณ ์œ ํ•œ ๋„ํŒŒ์‹œ๊ฐ„ ์ฐจ์ด๋ฅผ ๊ณ„์‚ฐํ•ด์„œ ์ˆ˜์ง‘ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ธฐ์กด ์ „ํ•˜ ๋ถ„๋ฐฐ ๋ถ€ํ˜ธํ™” ํšŒ๋กœ์™€ ๋‹ฌ๋ฆฌ ์‹ ํ˜ธ๊ฐ€ ์™œ๊ณก๋˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ๋ฌธํ„ฑ ์ „์•• ๋ฐฉ๋ฒ• (time-over-threshold; ToT) ๋ฐฉ์‹์œผ๋กœ ์—๋„ˆ์ง€๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ์…‹์งธ๋กœ, FPGA๋งŒ์œผ๋กœ ์•„๋‚ ๋กœ๊ทธ ์‹ ํ˜ธ๋ฅผ ๋””์ง€ํ„ธํ™” ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. FPGA์˜ ํ”„๋กœ๊ทธ๋žจ ๊ฐ€๋Šฅ ์ž…์ถœ๋ ฅํฌํŠธ๋ฅผ stub-series terminated logic (SSTL) ์ˆ˜์‹ ๊ธฐ๋กœ ํ”„๋กœ๊ทธ๋žจํ•˜๋ฉด, ๊ฐ๊ฐ์˜ FPGA ์ž…์ถœ๋ ฅํฌํŠธ๊ฐ€ ๋น ๋ฅธ ์‹œ๊ฐ„ ๋ฐ˜์‘์„ฑ์„ ๊ฐ€์ง„ ๊ณ ์„ฑ๋Šฅ ์ „์••๋น„๊ต๊ธฐ๋กœ ๋™์ž‘ํ•œ๋‹ค. ๋น„์ •์‹œ๊ฐ„ (time-of-flight; TOF) ์ธก์ • ๊ฐ€๋Šฅ PET ๊ฒ€์ถœ๊ธฐ์˜ ์‹ ํ˜ธ๋ฅผ ์ „๋‹จํšŒ๋กœ ์—†์ด FPGA๋งŒ์œผ๋กœ ๋””์ง€ํ„ธํ™”ํ•˜์—ฌ FPGA๋ฅผ ๊ณ ์„ฑ๋Šฅ DAQ ์žฅ์น˜๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ์ž…์ฆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ณต๊ฐ„๋ถ„ํ•ด๋Šฅ์ด ๋›ฐ์–ด๋‚œ ๋‡Œ์ „์šฉ ์Šค์บ๋„ˆ๋กœ๋ถ€ํ„ฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๊ธฐ ์œ„ํ•ด ์ „์••๋น„๊ต๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š” ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ DAQ ์žฅ์น˜๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์—๋„ˆ์ง€ ์ธก์ • ์ฑ„๋„์€ ์‹œ๊ฐ„-์ „ํ•˜ ๋ณ€ํ™˜๊ธฐ (charge-to-time converter; QTC)์™€ FPGA์˜ SSTL ์ˆ˜์‹ ๊ธฐ๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์‹œ๊ฐ ์ธก์ • ์ฑ„๋„์€ FPGA ๊ธฐ๋ฐ˜ TDC๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ๋‡Œ์ „์šฉ ์Šค์บ๋„ˆ์™€ ๊ณ ์ง‘์  ์‹œ๊ฐ„ ๊ธฐ๋ฐ˜ DAQ ์žฅ์น˜๋กœ ํš๋“ํ•œ ๋‡Œ๋ชจ์–‘ ํŒฌํ…€์˜ ์ž์„ธํ•œ ๊ตฌ์กฐ๋“ค์€ ์ž˜ ๊ตฌ๋ถ„๋˜์—ˆ๋‹ค.Chapter 1. Introduction 1 1.1. Background 1 1.1.1. Positron Emission Tomography 1 1.1.2. Silicon Photomultiplier 1 1.1.3. Data Acquisition System 2 1.1.4. Time-based Signal Digitization Method 3 1.2. Purpose of Research 6 Chapter 2. FPGA-based Time-to-Digital Converter 8 2.1. Background 8 2.2. Materials and Methods 9 2.2.1. Tapped-Delay-Line TDC 9 2.2.2. FPGA 11 2.2.3. Dual-Phase TDL TDC with On-the-Fly Calibrator 11 2.2.3.1. FPGA Clock Distribution Network 11 2.2.3.2. The Principle of Dual-Phase TDL TDC 14 2.2.3.3. The Principle of Pipelined On-the-Fly Calibrator 16 2.2.3.4. Implementation of Dual-Phase TDL TDC with On-the-Fly Calibrator 18 2.2.4. Experimental Setups and Data Processing 20 2.2.4.1. TDC Characteristics 21 2.2.4.2. Arrival Time Difference Measurements 22 2.3. Results 24 2.3.1. TDC Characteristics 24 2.3.2. Arrival Time Difference Measurements 25 2.4. Discussion 28 Chapter 3. Time-based Multiplexing Method 29 3.1. Background 29 3.2. Materials and Methods 30 3.2.1. Delay Grid Multiplexing 30 3.2.2. Detector for Concept Verification 32 3.2.3. Front-end Electronics 34 3.2.4. Experimental Setups 35 3.2.4.1. Data Acquisition Using the Waveform Digitizer 37 3.2.4.2. Data Acquisition Using the FPGA-TDC 37 3.2.5. Data Processing and Analysis 38 3.2.5.1. Waveform Digitizer 38 3.2.5.2. FPGA-TDC 41 3.3. Results 44 3.3.1. Waveform Digitizer 44 3.3.1.1. Waveform, Rise Time, and Decay Time 44 3.3.1.2. Flood Map 46 3.3.1.3. Energy 48 3.3.1.4. CTR 49 3.3.2. FPGA-TDC 50 3.3.2.1. ToT and Energy 50 3.3.2.2. Flood Map 51 3.3.2.3. CTR 52 3.4. Discussion 53 Chapter 4. FPGA-Only Signal Digitization Method 54 4.1. Background 54 4.2. Materials and Methods 56 4.2.1. Single-ended Memory Interface Input Receiver 56 4.2.2. SeMI Digitizer 56 4.2.3. Experimental Setup for Intrinsic Performance Characterization 59 4.2.3.1. ToT 59 4.2.3.2. Timing 60 4.2.4. Experimental Setup for Individual Signal Digitization 60 4.2.4.1. TOF PET Detector 60 4.2.4.2. Data Acquisition Using the Waveform Digitizer 61 4.2.4.3. Data Acquisition Using the SeMI Digitizer 63 4.2.4.4. Data Analysis 63 4.3. Results 64 4.3.1. Results of Intrinsic Performance Characterization 64 4.3.1.1. ToT 64 4.3.1.2. Timing 65 4.3.2. Results of Individual Signal Digitization 66 4.3.2.1. Energy 66 4.3.2.2. CTR 67 4.4. Discussion 68 Chapter 5. Comparator-less QTC DAQ Systems for High-Resolution Brain PET Scanners 70 5.1. Background 70 5.2. Materials and Methods 72 5.2.1. Brain PET Scanner 72 5.2.1.1. Block Detector 72 5.2.1.2. Sector 73 5.2.1.3. Scanner Geometry 74 5.2.2. Comparator-less QTC DAQ System 75 5.2.3. Data Acquisition Chain of Brain PET Scanner 79 5.2.4. Experimental Setups and Data Processing 79 5.2.4.1. Energy Linearity 79 5.2.4.2. Performance Evaluation of Block Detector 80 5.2.4.3. Phantom Studies 82 5.3. Results 83 5.3.1. Energy Linearity 83 5.3.2. Performance Evaluation of Block Detector 83 5.3.3. Phantom Studies 85 5.4. Discussion 87 Chapter 6. Conclusions 89 Bibliography 90 Abstract in Korean (๊ตญ๋ฌธ ์ดˆ๋ก) 94Docto

    A novel synchronizer for a 17.9ps Nutt Time-to-Digital Converter implemented on FPGA

    Get PDF
    The evolution of Field-Programmable Gate Array (FPGA) technology triggered the appearance of FPGAs with higher operating frequencies and large number of resources. Simultaneously, the evolution of the FPGAs design tools has simplified the development process, reducing the time to market. These factors made FPGA platforms attractive for several applications, including time-of-flight applications that require the implementation of Time-to-Digital Converters (TDC). This work presents a Nutt TDC, based on a coarse counter and a Tapped Delay Line, with 17.9 picoseconds resolution and 5.4 LSB differential nonlinearity (DNL), implemented in a Xilinx Zynq-7000 FPGA, to be used on LiDAR applications and pull-in time measuring in MEMS accelerometers systems.This work was supported by a Portuguese Scholarship from FCT - Fundaรงรฃo para a Ciรชncia e Tecnologia and Bosch Car Multimedia, under the Advanced Engineering Systems for Industry (AESI) doctoral program. (Scholarship ID: PDE/BDE/114562/2016) and COMPETE and FCT: POCI- 01-0145-FEDER-007043 within the Project Scope: UID/CEC/00319/201

    Strategies towards high performance (high-resolution/linearity) time-to-digital converters on field-programmable gate arrays

    Get PDF
    Time-correlated single-photon counting (TCSPC) technology has become popular in scientific research and industrial applications, such as high-energy physics, bio-sensing, non-invasion health monitoring, and 3D imaging. Because of the increasing demand for high-precision time measurements, time-to-digital converters (TDCs) have attracted attention since the 1970s. As a fully digital solution, TDCs are portable and have great potential for multichannel applications compared to bulky and expensive time-to-amplitude converters (TACs). A TDC can be implemented in ASIC and FPGA devices. Due to the low cost, flexibility, and short development cycle, FPGA-TDCs have become promising. Starting with a literature review, three original FPGA-TDCs with outstanding performance are introduced. The first design is the first efficient wave union (WU) based TDC implemented in Xilinx UltraScale (20 nm) FPGAs with a bubble-free sub-TDL structure. Combining with other existing methods, the resolution is further enhanced to 1.23 ps. The second TDC has been designed for LiDAR applications, especially in driver-less vehicles. Using the proposed new calibration method, the resolution is adjustable (50, 80, and 100 ps), and the linearity is exceptionally high (INL pk-pk and INL pk-pk are lower than 0.05 LSB). Meanwhile, a software tool has been open-sourced with a graphic user interface (GUI) to predict TDCsโ€™ performance. In the third TDC, an onboard automatic calibration (AC) function has been realized by exploiting Xilinx ZYNQ SoC architectures. The test results show the robustness of the proposed method. Without the manual calibration, the AC function enables FPGA-TDCs to be applied in commercial products where mass production is required.Time-correlated single-photon counting (TCSPC) technology has become popular in scientific research and industrial applications, such as high-energy physics, bio-sensing, non-invasion health monitoring, and 3D imaging. Because of the increasing demand for high-precision time measurements, time-to-digital converters (TDCs) have attracted attention since the 1970s. As a fully digital solution, TDCs are portable and have great potential for multichannel applications compared to bulky and expensive time-to-amplitude converters (TACs). A TDC can be implemented in ASIC and FPGA devices. Due to the low cost, flexibility, and short development cycle, FPGA-TDCs have become promising. Starting with a literature review, three original FPGA-TDCs with outstanding performance are introduced. The first design is the first efficient wave union (WU) based TDC implemented in Xilinx UltraScale (20 nm) FPGAs with a bubble-free sub-TDL structure. Combining with other existing methods, the resolution is further enhanced to 1.23 ps. The second TDC has been designed for LiDAR applications, especially in driver-less vehicles. Using the proposed new calibration method, the resolution is adjustable (50, 80, and 100 ps), and the linearity is exceptionally high (INL pk-pk and INL pk-pk are lower than 0.05 LSB). Meanwhile, a software tool has been open-sourced with a graphic user interface (GUI) to predict TDCsโ€™ performance. In the third TDC, an onboard automatic calibration (AC) function has been realized by exploiting Xilinx ZYNQ SoC architectures. The test results show the robustness of the proposed method. Without the manual calibration, the AC function enables FPGA-TDCs to be applied in commercial products where mass production is required

    Efficient time-to-digital converters in 20 nm FPGAs with wave union methods

    Get PDF
    The wave union (WU) method is a well-known method in time-to-digital converters (TDCs) and can improve TDC performances without consuming extra logic resources. However, a famous earlier study concluded that the WU method is not suitable for UltraScale field-programmable gate array (FPGA) devices, due to more severe bubble errors. This paper proves otherwise and presents new strategies to pursue high-resolution TDCs in Xilinx UltraScale 20 nm FPGAs. Combining our new sub-tapped delay line (sub-TDL) architecture (effective in removing bubbles and zero-width bins) and the WU method, we found that the wave union method is still powerful in UltraScale devices. We also compared the proposed TDC with the TDC combining the dual sampling (DS) structure and the sub-TDL technique. A binning method is introduced to improve the linearity. Moreover, we derived a formula of the total measurement uncertainties for a single-stage TDL-TDC to obtain its root-mean-square (RMS) resolution. Compared with previously published FPGA-TDCs, we presented (for the first time) much more detailed precision analysis for single-TDL TDCs

    Multi-channel, low nonlinearity time-to-digital converters based on 20nm and 28nm FPGAs

    Get PDF
    Abstractโ€”This paper presents low nonlinearity, compact and multi-channel time-to-digital converters (TDC) in Xilinx 28nm Virtex 7 and 20nm UltraScale FPGAs. The proposed TDCs integrate several innovative methods that we have developed: 1) the sub-tapped delay line (TDL) averaging topology, 2) tap timing tests, 3) a direct compensation architecture and 4) a mixed calibration method. The code density tests show that the proposed TDCs have much better linearity performances than previously reported ones. Our approach is cost-effective in terms of the consumption of logic resources. To demonstrate this, we implemented 96 channel TDCs in both FPGAs, using less than 25 % of the logic resources. The achieved least significant bit (LSB) is 10.5ps for Virtex 7 and 5.0 ps for UltraScale FPGAs. After the compensation and calibration, the differential nonlinearity (DNL) is within [-0.05, 0.08] LSB with ฯƒDNL = 0.01 LSB, and the integral nonlinearity (INL) is within [-0.09, 0.11] LSB with ฯƒINL = 0.04 LSB for the Virtex 7 FPGA. The DNL is within [-0.12, 0.11] LSB with ฯƒDNL = 0.03 LSB, and the INL is within [-0.15, 0.48] LSB with ฯƒINL = 0.20 LSB for the UltraScale FPGA

    128-channel high-linearity resolution- adjustable time-to-digital converters for LiDAR applications : software predictions and hardware implementations

    Get PDF
    This paper proposes a new calibration method, called the mixed-binning (MB) method, to pursue high-linearity time-to-digital converters (TDCs) for light detection and ranging (LiDAR) applications. The proposed TDCs were developed using tapped delay-line (TDL) cells in field-programmable gate arrays (FPGAs). With the MB method, we implemented a resolution-adjustable TDC showing excellent linearity in Xilinx UltraScale FPGAs. We demonstrate a 128-channel TDC to show that the proposed method is cost-effective in logic resources. We also developed a software tool to predict the performances of TDL-based TDCs robustly. Results from both software analysis and hardware implementations are in a good agreement and show that the proposed design has great potential for multichannel applications; the averaged DNL_(pk-pk) and INL_(pk-pk) are close to or even less than 0.05 LSB in multichannel designs
    • โ€ฆ
    corecore