Cui – Best Paper IEEE T-CPMT
The editors of the journal selected Cui’s paper “Stochastic Collocation with Non-Gaussian Correlated Process Variations: Theory, Algorithms and Applications” in the Electrical Performance of Integrated Systems category.
Today’s electronic and photonic integrated circuit chips are subject to increasing process variation due to the imprecise nanoscale fabrication. Due to these process variations, some chips have good performance, some have bad performance, and some fail to work. Therefore, how to quantify and control the impact of process variations in the design flow is a critical problem in both academia and industry. While numerous algorithms and tools have been developed in the design automation community, handling non-Gaussian correlated process variations is a long-standing challenge for both theorists and software designers. Their paper proposed a novel approach to solve this challenging problem with both high computational efficiency and rigorous theoretical performance guarantees.
Chunfeng Cui’s research is mainly focused in the areas of uncertainty quantification, tensor computations, and machine learning. She received her Ph.D. degree in 2016 from Chinese Academy of Sciences in Beijing, China and joined ECE department as a postdoc in November 2017. Dr. Cui received the Best Paper Award of the IEEE EPEPS 2018, the Zhongjiaqing mathematics award in China 2019, and was selected as one of the rising stars in Computational Data Sciences 2019 and rising stars in EECS 2019.
Since the June 2020 IEEE 70th Electronic Components and Technology Conference (ECTC) has been moved to a virtual platform, Cui will be recognized for the paper at next year’s ECTC event. The Electronic Components and Technology Conference is the premier international event that brings together the best in packaging, components and microelectronic systems science, technology and education in an environment of cooperation and technical exchange. ECTC is sponsored by the IEEE Electronics Packaging Society.
“Stochastic Collocation with Non-Gaussian Correlated Process Variations: Theory, Algorithms and Applications,” IEEE Transactions on Components, Packaging and Manufacturing Technology (Volume: 9 , Issue: 7 , July 2019)