{"title":"基于机器学习的时隙 ADC 失配校准","authors":"Jiajun Qin;Wentao Zhong;Yi Cao;Jiaming Li;Zhe Cao;Lei Zhao","doi":"10.1109/TNS.2024.3422277","DOIUrl":null,"url":null,"abstract":"The time-interleaved analog-to-digital conversion (TIADC) technique provides an effective way to achieve high sampling speed. However, a critical challenge in TIADC design arises from the presence of mismatches among parallel sub-analog-to-digital converters (ADCs), which detrimentally affect system performance. In this article, we propose a machine-learning-based method to address these mismatches across a broadband of input signal frequencies. Different from conventional approaches, this method avoids complex and specific matrix operations and reduces the compensation filter order required to achieve a given reconstruction accuracy. To assess the efficacy of our proposed method, we designed a 5-Gs/s 12-bit TIADC system. Through extensive testing, the results demonstrate notable improvements in the effective number of bits (ENOBs) following real-time calibration. Specifically, for input frequencies below 500 MHz, the ENOB surpasses 9 bits, while for frequencies ranging from 500 MHz to 1.25 GHz, it exceeds 8 bits.","PeriodicalId":13406,"journal":{"name":"IEEE Transactions on Nuclear Science","volume":"71 8","pages":"2012-2019"},"PeriodicalIF":1.9000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine-Learning-Based Mismatch Calibration for Time-Interleaved ADCs\",\"authors\":\"Jiajun Qin;Wentao Zhong;Yi Cao;Jiaming Li;Zhe Cao;Lei Zhao\",\"doi\":\"10.1109/TNS.2024.3422277\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The time-interleaved analog-to-digital conversion (TIADC) technique provides an effective way to achieve high sampling speed. However, a critical challenge in TIADC design arises from the presence of mismatches among parallel sub-analog-to-digital converters (ADCs), which detrimentally affect system performance. In this article, we propose a machine-learning-based method to address these mismatches across a broadband of input signal frequencies. Different from conventional approaches, this method avoids complex and specific matrix operations and reduces the compensation filter order required to achieve a given reconstruction accuracy. To assess the efficacy of our proposed method, we designed a 5-Gs/s 12-bit TIADC system. Through extensive testing, the results demonstrate notable improvements in the effective number of bits (ENOBs) following real-time calibration. Specifically, for input frequencies below 500 MHz, the ENOB surpasses 9 bits, while for frequencies ranging from 500 MHz to 1.25 GHz, it exceeds 8 bits.\",\"PeriodicalId\":13406,\"journal\":{\"name\":\"IEEE Transactions on Nuclear Science\",\"volume\":\"71 8\",\"pages\":\"2012-2019\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Nuclear Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10580961/\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Nuclear Science","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10580961/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Machine-Learning-Based Mismatch Calibration for Time-Interleaved ADCs
The time-interleaved analog-to-digital conversion (TIADC) technique provides an effective way to achieve high sampling speed. However, a critical challenge in TIADC design arises from the presence of mismatches among parallel sub-analog-to-digital converters (ADCs), which detrimentally affect system performance. In this article, we propose a machine-learning-based method to address these mismatches across a broadband of input signal frequencies. Different from conventional approaches, this method avoids complex and specific matrix operations and reduces the compensation filter order required to achieve a given reconstruction accuracy. To assess the efficacy of our proposed method, we designed a 5-Gs/s 12-bit TIADC system. Through extensive testing, the results demonstrate notable improvements in the effective number of bits (ENOBs) following real-time calibration. Specifically, for input frequencies below 500 MHz, the ENOB surpasses 9 bits, while for frequencies ranging from 500 MHz to 1.25 GHz, it exceeds 8 bits.
期刊介绍:
The IEEE Transactions on Nuclear Science is a publication of the IEEE Nuclear and Plasma Sciences Society. It is viewed as the primary source of technical information in many of the areas it covers. As judged by JCR impact factor, TNS consistently ranks in the top five journals in the category of Nuclear Science & Technology. It has one of the higher immediacy indices, indicating that the information it publishes is viewed as timely, and has a relatively long citation half-life, indicating that the published information also is viewed as valuable for a number of years.
The IEEE Transactions on Nuclear Science is published bimonthly. Its scope includes all aspects of the theory and application of nuclear science and engineering. It focuses on instrumentation for the detection and measurement of ionizing radiation; particle accelerators and their controls; nuclear medicine and its application; effects of radiation on materials, components, and systems; reactor instrumentation and controls; and measurement of radiation in space.