IEEE Open Journal of the Solid-State Circuits Society最新文献_第10页

2022 Index IEEE Open Journal of the Solid-State Circuits Society Vol. 2 2022年索引IEEE固态电路学会开放期刊第2卷

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2022-01-01 DOI: 10.1109/OJSSCS.2023.3265562

引用次数: 0

Call for Papers: RFIC 2023 论文征集：RFIC 2023

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2022-01-01 DOI: 10.1109/OJSSCS.2022.3218838

引用次数: 0

IEEE Open Journal of the Solid-State Circuits Society Instructions for Authors IEEE固态电路学会开放期刊作者须知

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2022-01-01 DOI: 10.1109/OJSSCS.2023.3234383

引用次数: 0

Guest Editorial Special Section on Electronic–Photonic Integrated Circuits (EPIC) 电子-光子集成电路（EPIC）客座编辑特刊

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-12-14 DOI: 10.1109/OJSSCS.2021.3130313

Firooz Aflatouni

引用次数: 0

Hybrid Time-of-Flight Image Sensors for Middle-Range Outdoor Applications 用于中程户外应用的混合飞行时间图像传感器

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-12-07 DOI: 10.1109/OJSSCS.2021.3133224

Shoji Kawahito;Keita Yasutomi;Kamel Mars

{"title":"Hybrid Time-of-Flight Image Sensors for Middle-Range Outdoor Applications","authors":"Shoji Kawahito;Keita Yasutomi;Kamel Mars","doi":"10.1109/OJSSCS.2021.3133224","DOIUrl":"https://doi.org/10.1109/OJSSCS.2021.3133224","url":null,"abstract":"This paper introduces a new series of time-of-flight (TOF) range image sensors that can be used for outdoor middle-range (10m to 100m) applications by employing a small duty-cycle modulated light pulse with a relatively high optical peak power. This set of TOF sensors is referred to here as a hybrid TOF (hTOF) image sensor. The hTOF image sensor is based on the indirect TOF measurement principle but simultaneously uses the direct TOF concept for coarse measurements. Compared to conventional indirect TOF image sensors for outdoor middle-range applications, the hTOF image sensor has a distinct advantage due to the reduction of capturing ambient light charge. To show the potential of the hTOF image sensor for outdoor middle-range operation, a model of estimating distance precision of hTOF image sensors is built and applied it by using possible sensor specifications to estimate the distance precision of the hTOF range camera in 10m, 20m and 40m measurements under the ambient-light condition of 100klux and its feasibility is discussed. In outdoor 10m-range measurements, the advantage of hTOF image sensors compared to the conventional indirect TOF image sensors is discussed by considering the amount of captured ambient-light charge in pixels.","PeriodicalId":100633,"journal":{"name":"IEEE Open Journal of the Solid-State Circuits Society","volume":"2 ","pages":"38-49"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782712/9733783/09638992.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67868117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Challenges and Trends of Nonvolatile In-Memory-Computation Circuits for AI Edge Devices 用于AI边缘设备的非易失性内存计算电路的挑战和趋势

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-10-26 DOI: 10.1109/OJSSCS.2021.3123287

Je-Min Hung;Chuan-Jia Jhang;Ping-Chun Wu;Yen-Cheng Chiu;Meng-Fan Chang

引用次数: 22

Fast Validation of Mixed-Signal SoCs 混合信号SoC的快速验证

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-10-25 DOI: 10.1109/OJSSCS.2021.3122397

Daniel Stanley;Can Wang;Sung-Jin Kim;Steven Herbst;Jaeha Kim;Mark Horowitz

引用次数: 4

A Spurless and Wideband Continuous-Time Electro-Optical Phase Locked Loop (CT-EOPLL) for High Performance LiDAR 用于高性能激光雷达的无杂散宽带连续时间电光锁相环（CT-EOPLL）

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-10-15 DOI: 10.1109/OJSSCS.2021.3120243

Ali Binaie;Sohail Ahasan;Harish Krishnaswamy

{"title":"A Spurless and Wideband Continuous-Time Electro-Optical Phase Locked Loop (CT-EOPLL) for High Performance LiDAR","authors":"Ali Binaie;Sohail Ahasan;Harish Krishnaswamy","doi":"10.1109/OJSSCS.2021.3120243","DOIUrl":"https://doi.org/10.1109/OJSSCS.2021.3120243","url":null,"abstract":"Frequency-modulated continuous-wave (FMCW) LiDAR systems are drawing increasing interest due to their potential applications in autonomous driving, machine perception, rapid prototyping, and medical diagnostics. The nonlinearity of a laser’s input-output transfer function can degrade the performance of an FMCW LiDAR. However, traditional discrete-time electro-optical phase-locked loops (DT-EOPLLs) face an unfavorable trade-off between chirp bandwidth and Mach-Zehnder delay. We present an integrated continuous-time electro-optic phase-locked loop (CT-EOPLL) to address this problem. The proposed EOPLL is very wideband, with its loop bandwidth equal to its reference frequency. This feature enables it to relax the trade-off between chirp bandwidth and Mach-Zehnder (MZ) delay by \u0000<inline-formula> <tex-math>$10times $ </tex-math></inline-formula>\u0000 in dB scale, which consequently reduces the area and loss associated with the silicon photonic delay implementation. It also does not suffer from the challenging issue of spurs in wideband PLLs because it features image and harmonic spur suppression in the loop using single-sideband (SSB) and harmonic-reject (HR) mixing techniques. The electrical part of this EOPLL is implemented in 65nm CMOS technology, and its optical integrated circuit is fabricated using a silicon photonic process. Featuring more than 25dB of suppression of the highest spur, this EOPLL is utilized in a high precision LiDAR sensor that shows an RMS depth precision of \u0000<inline-formula> <tex-math>$558~mu text{m}$ </tex-math></inline-formula>\u0000 at 2m distance, and a 9.4mm RMS depth resolution at ranges exceeding 3.3m.","PeriodicalId":100633,"journal":{"name":"IEEE Open Journal of the Solid-State Circuits Society","volume":"1 ","pages":"235-246"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782712/8816720/09576527.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67861569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Semiconductor-Based Monolithic Optical Phased Array Architectures 半导体单片光学相控阵结构综述

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-10-15 DOI: 10.1109/OJSSCS.2021.3120238

Hossein Hashemi

引用次数: 5

An Overview of Energy-Efficient Hardware Accelerators for On-Device Deep-Neural-Network Training 用于设备上深度神经网络训练的节能硬件加速器综述

IEEE Open Journal of the Solid-State Circuits Society Pub Date : 2021-10-13 DOI: 10.1109/OJSSCS.2021.3119554

Jinsu Lee;Hoi-Jun Yoo

{"title":"An Overview of Energy-Efficient Hardware Accelerators for On-Device Deep-Neural-Network Training","authors":"Jinsu Lee;Hoi-Jun Yoo","doi":"10.1109/OJSSCS.2021.3119554","DOIUrl":"https://doi.org/10.1109/OJSSCS.2021.3119554","url":null,"abstract":"Deep Neural Networks (DNNs) have been widely used in various artificial intelligence (AI) applications due to their overwhelming performance. Furthermore, recently, several algorithms have been reported that require on-device training to deliver higher performance in real-world environments and protect users’ personal data. However, edge/mobile devices contain only limited computation capability with battery power, so an energy-efficient DNN training processor is necessary to realize on-device training. Although there are a lot of surveys on energy-efficient DNN inference hardware, the training is quite different from the inference. Therefore, analysis and optimization techniques targeting DNN training are required. This article aims to provide an overview of energy-efficient DNN processing that enables on-device training. Specifically, it will provide hardware optimization techniques to overcomes the design challenges in terms of distinct dataflow, external memory access, and computation. In addition, this paper summarizes key schemes of recent energy-efficient DNN training ASICs. Moreover, we will also show a design example of DNN training ASIC with energy-efficient optimization techniques.","PeriodicalId":100633,"journal":{"name":"IEEE Open Journal of the Solid-State Circuits Society","volume":"1 ","pages":"115-128"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782712/8816720/09569757.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67860280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9