Cell library characterization using machine learning for design technology co-optimization

Abstract

To explore the full potential of any circuit and ensure its functionality at run-time, cell libraries beyond the typical PVT corners are needed. This holds even more for emerging technologies like Negative Capacitance (NC)-FinFET, where research in finding the optimal set of transistor parameters is still in its infancy. Design Technology Co-Optimization (DTCO) tackles bridging the large existing gap between device physics and the figures of merit of circuits. In this paper, we propose a Machine Learning (ML) approach to rapidly generate full cell libraries on demand. This enables the designer to perform extensive design space exploration and fully automated Design Technology Co-Optimization while lowering the barrier of accessibility. We demonstrate library prediction with an R2 score of around 98% for individual values and Static Timing Analysis (STA) reports. Experimental results show that our DTCO approach overestimates the achievable improvement by around 5%, nevertheless improving upon the baseline configuration.