MAXCell: PPA-Directed Multi-Height Cell Layout Routing Optimization using Anytime MaXSAT with Constraint Learning

To optimize power, performance, and area (PPA) of IC designs, standard cell has evolved from basic to complicated designs, resulting in complex multi-height structures. Although extensive research on single-height cell automatic synthesis, multi-height cell studies are still limited due to the extremely large solution space. In this paper, we present MAXCell, a PPA-directed standard cell layout optimization framework for both single-height and multi-height designs using anytime MaxSAT with constraint learning. This framework incorporates two novel techniques: (1) learning additional constraints from the original constraints database to accelerate convergence during problem-solving and (2) integrating a genetic algorithm with a ranking model to dynamically guide the router towards the PPA goal directly during optimization. Experimental results indicate that MAXCell outperforms previous studies that target wire length optimization, achieving a 5.5% power reduction in evaluations of 33 multi-bit flip-flop designs beyond 4nm technology. Furthermore, compared to an industrial library designed by experienced engineers, MAXCell provides a 3.5% power optimization benefit and drastically reduces the delivery time from multiple days to a mere few hours (21.6X faster). This emphasizes its efficiency and its potential in modern integrated circuit design.