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

Abstract

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.

Efficient mask characterization through automated contour and corner rounding extraction

“…Advances in pattern fidelity and process control, as reflected by Edge Placement Error (EPE) and CDU, are key enablers for yield improvements and scaling to smaller dimensions. Thus, mask dimensions characterization and control are increasingly important [1][2][3][4] . To reduce non-uniformities on wafer, it is crucial to ensure required quality and uniformity already on mask patterns by appropriate control of the mask manufacturing process.…”