Increasing productivity in the area of analog integrated circuit design requires the development of a new generation of circuit synthesis tools. A major challenge is that continued scaling of transistor dimensions following Moore's Law makes it difficult to describe the physical behavior of transistors in the form suitable for circuit optimization. The work under this proposal will develop a new approach for optimization over approximate descriptions of transistor behavior, which is the only realistic way to capture analog circuit behavior in a manner appropriate for automated synthesis. The approach is based on explicitly modeling the divergence between the exact model and the approximate model. The research will specifically develop: (1) a new analog synthesis framework for model-based optimization over approximate functions that is able to explicitly take into account the distribution of errors between the approximate and exact models to drive optimization to a solution guaranteed to be true with respect to the exact model; (2) a new model-fitting algorithm, tailored for highly-constrained optimization over approximate functions, that will further enhance the ability of the synthesis tool to produce a good solution.

The outcomes of the work under this proposal will lead to increased automation of analog and mixed-signal design, and result in higher design productivity, as well as more power-efficient and cheaper integrated circuits. Thus, this work will help sustain the evolution and growth of semiconductor technology that has had enormous social implications over the last fifty years. The concepts to be developed will also benefit other scientific domains in which optimization using approximate functions is used. The educational component of this proposal aims to combine the active research program and research experience in this field with the creation of an instructional and teaching infrastructure. Specifically, the graduate courses offered by the PIs will incorporate the aspects of design methodologies developed in this research.

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University of Texas Austin
United States
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