Multi-Source Transfer Learning for Design Technology Co-Optimization

Abstract

In advanced technology nodes, pitch scaling have not kept up with the Moore's Law. To continue progression, the design technology co-optimization (DTCO) has been proposed. However, implementing DTCO requires significant time cost and resources due to iterative trials. In addition, optimal design and technology option depend on each design, thus it should start from scratch whenever the target design changes. We present a DTCO framework based on Bayesian optimization that efficiently explores design feedback for optimization. In addition, our framework incorporates a multi-source transfer Gaussian process (MTGP) that ensures robust optimization even for unseen designs. MTGP significantly improves prediction and generalization performance by integrating multiple single source transfer Gaussian processes. Our framework, on average, reduced the mean absolute error of power and area by 47.3% and 24.1%, respectively, and power and area by 37.3% and 19.9%, respectively, compared to the reference, in 7nm technology nodes.