Data storage is essential to a broad range of electronic and biological systems. Static random access memories (SRAMs) provide essential on-chip data storage for many electronic applications including microprocessors, ASICs, DSPs and SoCs. There also exists a growing effort in developing engineered genetic memory circuits to facilitate new understanding of cellular phenomena in natural organisms, cellular control and biocomputing. This work intends to facilitate the understanding, analysis and enhancement of dynamic stability, a key system property, for semiconductor SRAMs, emerging memristor and biological memories. Conventional static SRAM stability metrics are unable to capture intrinsic dynamic circuit operations and hence inherently limited in their applications. This work addresses the need for a rigorous understanding of dynamic stability in semiconductor and genetic memories via a system-theoretic approach. Nonlinear system theory will be exploited to construct new dynamic noise margin concepts and design metrics with theoretic rigor and design insights. Novel system theoretically motivated numerical algorithms will be developed to facilitate analysis and optimization with significantly improved efficiency.
This work will facilitate the design of nanoscale computing systems as well as the development of synthetic gene-regulatory networks. Interdisciplinary explorations will provide new opportunities for solving research problems of practical significance and offer educational opportunities to students. The PIs will promote the research participation from undergraduate students and students from underrepresented groups and engage in high-school teacher enrichment programs. The research outcomes will be integrated into undergraduate and graduate curriculum and disseminated in the research community and major semiconductor companies.
