Fast lithographic source optimization method of certain contour sampling-Bayesian compressive sensing for high fidelity patterning

Fast source optimization (SO) is in demand urgently for holistic lithography on-line at 14-5 nm nodes. Our earlier works of fast compressive sensing (CS) SO methods adopted randomly sampling monitoring pixels on layout patterns, consequently resulting in failure of SO sometimes and poor image fidelity compared to gradient-based SO with complete sampling (SD-SO). This paper proposes a novel certain contour sampling-Bayesian compressive sensing SO (CCS-BCS-SO) method to achieve the goals of fast SO and high fidelity patterns simultaneously. The CCS assures the optimized source uniquely and reduces the computational complexity significantly. The BCS theory, to our best knowledge, is for the first time applied to resolution enhancement techniques (RETs) in lithography systems to ensure high fidelity patterns. The results demonstrate that CCS-BCS-SO simultaneously achieves fast SO like CS-SO and high fidelity patterns like SD-SO.