A formulation of mask optimization into QUBO model for Ising machines
Yukihide Kohira, Haruki Nakayama, Naoki Nonaka, Tomomi Matsui, Atsushi Takahashi, Chikaaki Kodama · 2023
Recently, Ising machines, which solve Quadratic Unconstrained Binary Optimization (QUBO) problems in a short computation time, have attracted attention. In this paper, we propose a mask optimization method using an Ising machine to obtain a mask with high fidelity to target patterns and high tolerance to process variation. In the proposed method, a mask pattern is improved by repeatedly solving QUBO problems by the Ising machine. In experiments, we applied the proposed method using the Ising machine to various patterns and evaluated it in comparison with existing methods in fidelity to target patterns, tolerance to process variation, and execution time.