个人简介

2011年本科毕业于南京大学，后获得美国佐治亚理工学院计算机科学硕士和康奈尔大学光学工程博士学位。2017-2023年担任美国AMD半导体公司主任工程师、美国德克萨斯州立大学电子工程系客座教授。

邹宁睦博士近年来围绕人工智能算法在集成电路先进制程中的开发与应用开展系统性研究工作，提出了一系列针对3nm-7nm制程中光刻工艺优化、光掩模近场光学校正、器件良率及性能提高、芯片缺陷诊断与分类检测的机器学习模型及大数据分析方法，拓展了人工智能技术在芯片制造领域的应用，组织构建了世界领先水平的企业级智能化芯片制造感知大数据系统。同时，与国内院校和企业合作开发了多套光纤传感及智能诊断系统，用于大型设施的在线健康监测。邹博士曾在Nature、Nature子刊等期刊发表学术论文30余篇，授权专利14项。近年来，获得过2024年世界制造工程协会“杰出青年制造工程师奖”（SME Outstanding Young Manufacturing Engineer Award，当年中国大陆地区高校唯一获奖者)，2022年“全美十大华人杰出青年”称号，2019年AMD全球研发大会最佳论文奖，2018年AMD亚洲研发大会最佳创新奖，两次获得AMD Annual Executive Spotlight Award，此外还获得过2022年中国仪器仪表学会技术发明一等奖、2022年中国光电工程学会“金燧奖”、2023年中国安装协会科学技术进步一等奖等奖项，同时担任过Photonics Asia, IEEE VLSI, ISTFA等多个国际会议学术委员会委员。

课题组现招收硕士博士若干，课题组网站：https://zouningmu.github.io/。联系邮箱：nzou@nju.edu.cn

英文简介

Dr. Zou Ningmu is an Associate Professor in the School of Integrated Circuits at Nanjing University. He completed his undergraduate studies at Nanjing University in 2011 and obtained M.S. degree in Computer Science from the Georgia Institute of Technology, and Ph.D. in Optical Engineering from Cornell University. From 2017 to 2023, he served as a Staff Engineer at AMD Inc. at Texas, US and was also an adjunct professor in the Department of Electrical Engineering at Texas State University.

In recent years, Dr. Zou has conducted systematic research on the development and application of artificial intelligence algorithms in advanced semiconductor processes. He has proposed a series of machine learning models and big data analysis methods for optimizing photolithography processes, near-field optical mask correction, improving device yield and performance, and chip defect diagnosis and classification detection in the 3nm-7nm process range. His work has expanded the application of AI technology in the field of chip manufacturing and led to the establishment of a world-leading enterprise-level intelligent chip manufacturing perception big data system. He also collaborated with institutions and enterprises to develop multiple sets of fiber-optic sensing and intelligent diagnostic systems for online health monitoring of large facilities.