Lithographic source optimization based on adaptive projection compressive sensing.

This paper proposes to use the a-priori knowledge of the target layout patterns to design data-adaptive compressive sensing (CS) methods for efficient source optimization (SO) in lithography systems. A set of monitoring pixels are selected from the target layout based on blue noise random patterns. The SO is then formulated as an under-determined linear problem to improve image fidelity according to the monitoring pixels. Adaptive projections are then designed, based on the a-priori knowledge of the target layout, in order to further reduce the dimension of the optimization problem, while trying to retain the SO performance. Different from traditional CS methods, adaptive projections are constructed directly from the target layout data via a nonlinear thresholding operation. Adaptive projections are proved to achieve superior SO performance over the random projections. This paper also studies and compares the impact of different sparse representation bases on the SO performance. It is shown that the discrete cosine transform (DCT), spatial and Haar wavelet bases are good choices for source representation.