SHF: Small: Bayesian Model Fusion: A Statistical Framework for Efficient Validation and Tuning of Complex Analog and Mixed Signal Circuits

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Adaptive self-healing is an emerging methodology to combat the deleterious effects of nanoscale process variations, to maintain the aggressive scaling of analog and mixed-signal (AMS) circuits. Nowadays, each tunable AMS circuit becomes a large-scale, complex system that can adaptively vary over time. The prohibitively high cost associated with pre-silicon validation and post-silicon tuning of such a complex system is a growing problem as devices continue to shrink and the relative magnitude of critical process fluctuations continues to grow. Hence, there is an immediate need to develop new statistical methodologies that minimize the validation and tuning cost of nanoscale AMS circuits for future technology generations. This project develops a novel statistical framework, referred to as Bayesian Model Fusion (BMF), that aims to minimize the simulation and/or measurement cost for both pre-silicon validation and post-silicon tuning of self-healing AMS circuits. The proposed BMF technique is motivated by the fact that today's AMS design cycle typically spans multiple stages (e.g., schematic design, layout design, first tape-out, second tape-out, etc). The key idea is to reuse the simulation and/or measurement data collected at an early stage to facilitate efficient validation and tuning of AMS circuits with a minimal amount of data required at the late stage. It provides a fundamental infrastructure that enables next-generation AMS design for future IC technology. The proposed project offers a radically new AMS design methodology based on Bayesian inference. It is expected to yield significant performance improvement for advanced electrical circuits in a broad range of applications, from consumer electronics to medical instruments. Hence, successful development of the proposed BMF framework will have both short-term and long-term impacts on the semiconductor industry. In addition, the education activities integrated with this project offer a number of unique training opportunities to both university students and industrial engineers. It will substantially improve the education infrastructure and generate high-quality researchers and practitioners in the field.