MOIL: An Efficient Multi-objective Optimization Framework for SRAM Cell with Incremental Learning

This paper proposes MOIL, an efficient multi-objective optimization framework for SRAM design that combines neural network (NN) surrogate modeling with NSGA-II evolutionary algorithms. The framework features: 1) An NN surrogate model employing a two-phase training strategy — initially trained on Latin Hypercube Sampling data and progressively refined through incremental learning cycles — achieving 99.83% prediction accuracy on critical SRAM metrics (read/write delay, SNM, leakage power) while eliminating iterative SPICE simulations; 2) An adaptive NSGA-II optimizer incorporating dynamic crowding distance calculation to effectively explore 5-dimensional device parameters (Fin width/height, Lg, PHIG) and generate Pareto-optimal solutions. Experimental results demonstrate MOIL's superior efficiency with 20.8× faster decision-making and 13.7× speedup over Bayesian optimization method, while maintaining solution diversity (hypervolume ratio of 3.52). The framework establishes a robust methodology for rapid SRAM evaluation and optimization in advanced technology nodes.