Exploring Rule-Free Layout Decomposition via Deep Reinforcement Learning

Multiple patterning lithography decomposition (MPLD) and mask optimization enable the ever-shrinking device feature sizes far below the lithography system limit. Conventional MPLD is solved by mathematical programming or graph-based approaches, where a set of predetermined rules is indispensable to identify the conflicts to be resolved. In this article, we explore rule-free layout decomposition following a simple but sweet principle, let the mask optimizer "teach" the layout decomposer how to generate suitable decompositions. Our flow includes a reinforcement-learning-based layout decomposer and a deep-learning-based mask optimizer. Without any handcrafted rules, our framework can perform competitively and even surpass the state-of-the-art rule-based methods with notable $(7\times \sim 63\times)$ turn-around-time speedup.