SHF: Medium: Collaborative Research: System Level Self Correction Using On-Chip Micro Sensor Network and Autonomous Feedback Control

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With technology scaling and increasing integration density in the nanometer technology regime, design considerations for yield and reliability have become critical. The objective of this collaborative research is to explore low-overhead formal design methodology with distributed micro-scale sensor network and systematic feedback control to achieve auto-curing of digital, analog and mixed-signal electronic systems under large process and temporal variations. Such auto-curing approaches will play a key role in preventing yield loss for nanoscale designs, while ensuring reliability of operation and low power dissipation. The research investigates self-curing concepts/techniques for logic circuits, digital signal processing (DSP) units, embedded memory and analog components using appropriate variation sensing and compensation techniques to achieve high yield with optimal power/die-area overhead. It also explores system-level self-curing approaches using global parameter sensor and global controller to determine optimal compensation of mixed-signal cores under power constraint. To realize the curing methodologies in an automatic synthesis environment, the research will aim at developing appropriate Computer-Aided Design tools and a library of self-correcting mixed-signal cores. If successful, it will help the semiconductor industry deliver complex nanoelectronic systems with high reliability, low power and high yield. The proposed research will integrate education and training through course development, summer research program for undergraduates, and senior project design.