Dr. Chengying Chen
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
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Academic Career Length
11Years (2016-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
ZCZhifeng Chen
Zhifeng ChenJCJiming Chen
Jiming ChenWLWenli Liao
Wenli LiaoYZYuan Zhao
Yuan ZhaoJJJianhua Jiang
Jianhua JiangChengying ChenAs the traditional silicon-based CMOS technology advances into the nanoscale stage, approaching its physical limits, the Carbon Nanotube Field-effect Transistor (CNTFET) is considered to be the most significant transistor technology beyond Moore’s era. The CNTFET has a quasi-one-dimensional structure so that the carrier can realize ballistic transport and has very high mobility. At the same time, a single CNTFET can integrate hundreds of nanowires as the conductive channels, enabling significant current transport capabilities even in low supply voltage, thereby providing a foundational basis for achieving nanoscale ultra-large-scale analog/logic circuits. This paper summarizes the development status of the CNTFET compact model and digital/analog/RF integrated circuits. The challenges faced by SPICE modeling and circuit design are analyzed. Meanwhile, solutions to these challenges and development trends of carbon-based transistors are discussed. Finally, the future application prospects of carbon-based integrated circuits are presented.
Journal Article
YHYUFEI HUANG
YUFEI HUANGCCChengying Chen
Chengying ChenYYYaguang Yang
Yaguang YangChengying ChenThere is always a need for low-power, area-efficient VLSI (Very Large-Scale Integration) design and this need is increasing day by day. However, conventional design methods based on Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) devices and Complementary Metal-Oxide-Semiconductor Transistor (CMOS) technology cannot meet the performance requirements. The memristor, as a promising computing and memory integration device, offers a new research idea for conventional logic circuit structure and architecture innovation, given its non-volatility, scalability, low power consumption, fast switching speed, etc. This paper proposes a brief overview of the characteristics and current status of memristor-based logic circuits and analyzes their applications in numerical expression and memory. The benefits and drawbacks of various analog logic circuit structures are summarized and compared. In addition, some solution strategies for these issues are presented. Finally, this paper offers prospects for the applications of memristors in the logic implementation of large-scale memristor arrays, the novel structure of in-memory computing, and neural network computing.
1 May 2018 in Microelectronics Journal
Journal Article
Chengying ChenLCLiming Chen
Liming Chen
XWXinghua Wang
Xinghua Wang
FZFeng Zhang
Feng Zhang
Journal Article
JYJiayu Yin
Jiayu YinWLWenli Liao
Wenli LiaoYZYuyan Zhang
Yuyan ZhangJJJianhua Jiang
Jianhua JiangChengying ChenCombining the advantages of low-power consumption of static random access memory (SRAM) with high stability and nonvolatile of resistive memory (RRAM), an improved 8T2R nonvolatile SRAM (nvSRAM) memory cell was proposed in this paper. In order to solve the problem that data cannot be stored when SRAM is powered off, RRAM technology was introduced into SRAM to realize an SRAM with nonvolatile function. The differential mode was adopted to improve the data restoration speed. Meanwhile, a pre-decoding technology was proposed to realize fast address decoding, and a voltage-mode sensitive amplifier was used to achieve fast amplification of two bit lines, so as to improve the reading speed of the memory. An 8kb nvSRAM was implemented with a CMOS 28 nm 1P9M process. The simulation results show that when the power supply voltage was 0.9 V, the static/read/write noise margin was 0.35 V, 0.16 V and 0.41 V, respectively. The data storage time was 0.21 ns, and restoration time was 0.18 ns. The time for the whole system to read 1 bit of data was 5.2 ns.
11 December 2018 in Circuits, Systems, and Signal Processing
Journal Article
Chengying ChenCCChengying Chen
Chengying ChenJournal Article
QRQirui Ren
Qirui RenChengying ChenDDDanian Dong
Danian DongXXXiaoxin Xu
Xiaoxin XuYCYong Chen
Yong ChenFZFeng Zhang
Feng ZhangThis brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass FIR filter has been introduced in an EEG AFE, where the bio-plausible characteristics of RRAM are utilized to analyze signals in the analog domain with high efficiency. The preamp uses the symmetrical OTA structure, reducing power consumption while meeting gain requirements. The ripple suppression circuit greatly improves noise characteristics and offset voltage. The RRAM-based low-pass filter achieves a 40 Hz cutoff frequency, which is suitable for the analysis of EEG signals. The SAR ADC adopts a segmented capacitor structure, effectively reducing the capacitor switching power consumption. The chip prototype is designed in 40 nm CMOS technology. The overall power consumption is approximately 13 µW, achieving ultra-low-power operation.
About Me
Chengying Chen is a Professor at the School of Optoelectronics and Communication Engineering, Xiamen University of Technology. He worked as an assistant researcher at the Institute of Microelectronics, Chinese Academy of Sciences from 2007 to 2016. He received his bachelor's degree in Microelectronics and Solid-State Electronics from the Department of Electronic Engineering, Beijing Institute of Technology, from 2001/09 to 2005/06, his master's degree in Microelectronics and Solid-State Electronics from 2005/09 to 2007/06, and his doctorate degree in Electronics and Information from 2012/09 to 2016/06, from the Institute of Microelectronics, Chinese Academy of Sciences. He is a master's/doctoral supervisor at Beijing Institute of Technology, a "double hundred talent" of Xiamen City, a Class B talent of Xiamen City, and a reviewer of the National Natural Science Foundation of China. As the project leader and scientific research backbone, he presided over and participated in the research and design of more than 20 scientific research projects, including the National Natural Science Foundation, the Chinese Academy of Sciences Knowledge Innovation Project, the 02/03 National Major Project, the Chinese Academy of Sciences Knowledge Innovation Major Project, the 863 Project, and the Chinese Academy of Sciences Pioneer Major Project, and achieved many results.