Computational rule-based approach for corner correction of non-Manhattan geometries in mask aligner photolithography

In proximity mask aligner photolithography, diffraction of light at the mask pattern is the predominant source for image shape distortions such as line end shortening and corner rounding. One established method to mitigate the impact of diffraction is optical proximity correction. This method relies on a deliberate sub-resolution modification of photomask features to counteract such shape distortions, with the goal to improve pattern fidelity and uniformity of printed features. While previously considered for masks featuring only rectangular shapes in horizontal or vertical orientation, called Manhatten geometries, we demonstrate here the capabilities of computational mask aligner lithography by extending optical proximity correction to non-Manhattan geometries. We combine a rigorous simulation method for light propagation with a particle-swarm optimization to identify suitable mask patterns adapt to each occurring feature in the mask. The improvement in pattern quality is demonstrated in experimental prints. Our method extends the use of proximity lithography in optical manufacturing, as required in a multitude of micro-optical devices.