吕良剑

2016 ~ 2020  复旦大学  微电子学与固体电子学专业  理学博士

2010 ~ 2013  复旦大学  微电子学与固体电子学专业  理学硕士

2006 ~ 2010  南京大学  物理学专业  理学学士

2021 ~ 至今  华东师范大学  副教授

2020 ~ 2021  复旦大学  研究助理

2015 ~ 2016  复旦大学  研究助理

2013 ~ 2015  泰凌微电子  集成电路设计工程师

2010年在南京大学获物理学专业学士学位，2013年和2020年在复旦大学分别获微电子学与固体电子学专业硕士和博士学位，2021年起任华东师范大学副教授。在模拟混合信号集成电路设计领域具有10余年的科研及工作经验，最近的研究内容主要包括高通量无线脑机接口及智能感知芯片与系统。近5年来在SCI期刊及国际顶级会议发表文章20余篇，其中包括集成电路设计领域的顶级期刊TBioCAS、JSSC、TMTT等和顶级会议ISSCC、CICC、ESSCIRC、RFIC等，并以第一作者获得了2019年ISCAS生物医疗方向最佳论文奖和2020年ISCAS学生最佳论文奖。2020年入选上海脑科学与类脑研究中心首批“求索杰出青年”——“求索”青年研究员。

参加学会：IEEE会员，中国电子学会会员，中国神经科学学会。

担任审稿：IEEE TCAS-I，ISCAS等。

招生信息：

1. 招收博士研究生（集成电路设计方向）。

2. 招收学术型硕士研究生（微电子学与固体电子）和专业型硕士研究生（集成电路工程）。

欢迎具有微电子、电子工程、计算机等相关学科背景的同学加入！

研究方向：

1. 高通量无线脑机接口芯片与系统设计，包括神经信号放大器、模数转换器、电源管理、射频收发机、无线能量采集电路、神经信号数据压缩与处理算法。

2. 智能感知芯片与系统设计，研究传感器信号读出及近传感器计算的低功耗片上实现。

近年来代表性芯片成果（部分）：

本科生课程：《工程与社会》，《模拟电子线路及实验》。

进行中项目：

1. 国家自然科学基金青年科学基金项目，面向高通量神经接口的混合信号数据压缩技术研究，2023至2025，主持

2. 中国科学院战略性先导科技专项，基于感存算一体架构的神经接口芯片研究，2022至2024，主持

3. 临港实验室“求索杰出青年计划”开放课题，基于微芯片的光电融合多模态神经活动记与调控系统，2022至2024，主持
   

4. 上海市市级科技重大专项，脑机接口关键技术与核心器件，2021至2024，芯片项目负责人

5. 科技部科技创新2030——“脑科学与类脑研究”重大项目，神经信号处理芯片及集成系统，2021至2026，课题骨干
   

6. 上海市科委，脑态调控的神经机制研究，2020至2023，参与

已结题项目：

1. 上海市科委，类脑芯片与片上系统研究，2016至2020，参与

2. 上海市科委，基于微芯片技术的脑活动多道记录系统，2016至2019，参与

芯片设计：

1. R. Gan, L. Lyu* and C.-J. Richard Shi, A 7-Channel Bio-Signal Analog Front End Employing Single-End Chopping Amplifier Achieving 1.48 NEF, ESSCIRC 2023- IEEE 49th European Solid State Circuits Conference (ESSCIRC), Lisbon, Portugal, 2023, pp. 5-8, doi: 10.1109/ESSCIRC59616.2023.10268787.

2. H. Jiang et al., A 2.53 uW/channel Event-Driven Neural Spike Sorting Processor with Sparsity-Aware Computing-In-Memory Macros, 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA, 2023, pp. 1-5, doi: 10.1109/ISCAS46773.2023.10181615.

3. H. Ren et al., A 19 uW Blocker-Tolerant Wake-Up Receiver With −90–dBm Energy-Enhanced Sensitivity, in IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 10, pp. 4377-4392, Oct. 2023, doi: 10.1109/TMTT.2023.3267542.

4. G. Mu, L. Lyu, D. Ye, and C.-J. Richard Shi, A chopper amplifier with a low duty-cycle sub-sampling in the switched-capacitor integrator for noise reduction. Electronics Letters. Accepted.

5. L. Lyu, Q. Wang, Z. Huang, and X. Wu, An in situ digital background calibration algorithm for multi-channel R-βR ladder DACs, Journal of Electronic Science and Technology, vol. 20, no. 1, 2022.

6. R. Gan, L. Lyu, G. Mu, and C.-J. R. Shi, A neural recording analog front-end with exponentially tunable pseudo resistors and on-chip digital frequency calibration loop achieving 3.4% deviation of high-pass cutoff frequency in 5-to-500 Hz range, in 2022 IEEE Custom Integrated Circuits Conference (CICC), 2022: IEEE, pp. 1-2.

7. G. Mu, D. Ye, L. Lyu, X. Zhao, and C.-J. R. Shi, An 8-channel analog front-end with a PVT-insensitive switched-capacitor and analog combo DC servo loop achieving 300mV tolerance and 0.64 s recovery time to electrode-DC offset for physiological signal recording, in 2021 IEEE Custom Integrated Circuits Conference (CICC), 2021: IEEE, pp. 1-2.

8. L. Lyu et al., A fully-integrated 64-channel wireless neural interfacing soc achieving 110 dB AFE PSRR and supporting 54 Mb/s symbol rate, meter-range wireless data transmission,IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II), vol. 67, no. 5, pp. 831-835, 2020.

9. L. Lyu et al., A fully-integrated 64-channel wireless neural recording SoC achieving 110 dB AFE PSRR and supporting 54 Mb/s symbol rate, meter-range wireless data transmission, in 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020: IEEE, pp. 1-4.(Student best paper award)

10. L. Lyu, D. Ye, and C.-J. R. Shi, A 340 nW/channel 110 dB PSRR neural recording analog front-end using replica-biasing LNA, level-shifter assisted PGA, and averaged LFP servo loop in 65 nm CMOS, IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 4, pp. 811-824, 2020.

11. L. Lyu, Y. Wang, C. Chen, and C.-J. R. Shi, A 0.6 V 1.07 μW/Channel neural interface IC using level-shifted feedback, Integration, vol. 70, pp. 51-59, 2020.

12. R. Xu, D. Ye, L. Lyu, and C.-J. R. Shi, A 2.0-2.9 GHz digital ring-based injection-locked clock multiplier using a self-alignment frequency tracking loop for reference spur reduction, in 2020 IEEE Radio Frequency Integrated Circuits Symposium (RFIC), 2020: IEEE, pp. 11-14.

13. Y. Tu, R. Xu, D. Ye, L. Lyu, and C.-J. R. Shi, A 400 MHz, 8-bit, 1.75-ps resolution pipelined-two-step time-to-digital converter with dynamic time amplification, in 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020: IEEE, pp. 1-4.

14. L. Lyu, D. Ye, and C.-J. R. Shi, A 340nW/channel neural recording analog front-end using replica-biasing LNAs to tolerate 200mVpp interfere from 350mV power supply, in 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019: IEEE, pp. 1-4. (Best paper award of BioCAS track)

15. L. Lyu, Y. Wang, C. Chen, and C.-J. R. Shi, A low-voltage low-power multi-channel neural interface IC using level-shifted feedback technology, in Proceedings of the 24th Asia and South Pacific Design Automation Conference (ASP-DAC), 2019, pp. 13-14.

16. D. Ye, R. Xu, L. Lyu, and C.-J. R. Shi, A 2.46 GHz,− 88dBm Sensitivity CMOS passive mixer-first nonlinear receiver with > 50dB tolerance to in-band interferer, in 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019: IEEE, pp. 1-4.

17. D. Ye, Y. Wang, Y. Xiang, L. Lyu, H. Min, and C.-J. R. Shi, A wireless power and data transfer receiver achieving 75.4% effective power conversion efficiency and supporting 0.1% modulation depth for ASK demodulation, IEEE Journal of Solid-State Circuits (JSSC), vol. 55, no. 5, pp. 1386-1400, 2019.

18. Y. Wang, D. Ye, L. Lyu, Y. Xiang, H. Min, and C.-J. R. Shi, A 13.56 MHz wireless power and data transfer receiver achieving 75.4% effective-power-conversion efficiency with 0.1% ASK modulation depth and 9.2 mW output power, in 2018 IEEE International Solid-State Circuits Conference (ISSCC), 2018: IEEE, pp. 142-144.

智能感知：

1. Y. Chen et al., Review of the Intelligent Sensor-Memory-Control Fusion Systems. Advanced Sensor Research, 2: 2200034.

2. H. Wangxu, L. Lyu, H. Bi, and X. Wu, Flexible pressure sensor array with multi-channel wireless readout chip, Sensors, vol. 22, no. 10, p. 3934, 2022.

3. S. Y. Wang et al., Waterproof and Breathable Graphene-Based Electronic Fabric for Wearable Sensors. Adv. Mater. Technol. 2022, 7, 2200149.

4. L. Chen et al., High throughput in‐situ temperature sensor array with high sensitivity and excellent linearity for wireless body temperature monitoring, Small Structures.

5. X. Yao et al., High‐performance flexible humidity sensors for breath detection and non‐touch switches, Nano Select, 2022.

6. X. Liu et al., Graphene‐based hydrogel strain sensors with excellent breathability for motion detection and communication, Macromolecular Materials and Engineering, 2022.

• 2020年 ISCAS Student Best Paper Award（第一作者）

• 2019年 ISCAS Best Paper Award of the BioCAS Track（第一作者）