Scan Chain Clustering and Optimization with Constrained Clustering and Reinforcement Learning

Scan chains are used in design for test by providing controllability and observability at each register. Scan optimization is run during physical design after placement where scannable elements are re-ordered along the chain to reduce total wirelength (and power). In this paper, we present a machine learning based technique that leverages constrained clustering and reinforcement learning to obtain a wirelength efficient scan chain solution. Novel techniques like next-min sorted assignment, clustered assignment, node collapsing, partitioned Q-Learning and in-context start-end node determination are introduced to enable improved wire length while honoring design-for-test constraints. The proposed method is shown to provide up to 24% scan wirelength reduction over a traditional algorithmic optimization technique across 188 moderately sized blocks from an industrial 7nm design.