Fast lithographic source optimization adopting RMSProp with iterative shrinkage-thresholding algorithm compressive sensing for high fidelity patterning

Fast Source Optimization (SO) is a critical requirement for the 14-5nm node in integrated lithography online technology. Our previous research introduced Bayesian Compressed Sensing SO (CCS-BCS-SO), which effectively delivered high pattern fidelity. However, its processing speed still lags behind that of compressive sensing (CS) SO. This paper introduces the first application of the iterative shrinkage - thresholding algorithm with RMSProp (RMSProp-ISTA) in compressive sensing. This innovation aims to ensure a high-fidelity pattern while improve convergence speed and accelerating SO. The results indicate that the CCS-RMSProp-ISTA-SO method is three times faster than the CCS-BCS-SO method, achieving the fast SO like CS-SO and the high pattern fidelity of SD-SO.