Research Topics

Learning in VLSI CAD

Machine learning is a powerful computer science technique which can derive knowledge from big data, and provides prediction and matching. Since nanometer VLSI CAD problems have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning techniques in VLSI CAD.


Machine Learning

hotspots 

Selected recent publications:

  • [C57] Hang Zhang, Fengyuan Zhu, Haocheng Li, Evangeline F. Y. Young, Bei Yu, “Bilinear Lithography Hotspot Detection”, ACM International Symposium on Physical Design (ISPD), pp. 7–14, Portland, OR, Mar. 19–22, 2017. (paper) (Best Paper Award)


Deep Learning

hotspots 

Selected recent publications:



Combinatorial Algorithms in VLSI CAD

Many classical VLSI CAD problems can be extracted and formulated into challenging combinatorial optimization problems. We are heavily working to improve the state-of-the-art. Selected recent publications are listed as follows:


Detailed Placement

detailed-placement 

Selected recent publications:


Layout Decomposition

layout-decomposition 

Selected recent publications:


Layer Assignment

layer-assignment 

Selected recent publications:



Cyber Physical System

cyber-physical-systems 

Cyber-physical system (CPS) addresses the close interactions and feedback loop between the cyber components such as sensing systems and the physical components such as varying environment and energy systems. Currently we are working on data regression and calibration in smart building environment.

Selected recent publications:



Hardware Security

hardware-security 

Selected recent publications: