This project develops a "Quantum Annealing-based Framework for Reticle Optimization" using D-Wave quantum annealers to solve mask optimization challenges in semiconductor lithography. Traditional Inverse Lithography Technology (ILT) suffers from convergence issues, local minimum traps, and excessive computation time. We formulate ILT as Quadratic Unconstrained Binary Optimization (QUBO) problems, leveraging quantum properties for rapid global minimum search. The framework demonstrates quantum annealing's potential in semiconductor manufacturing optimization.

First-ever quantum computing application to semiconductor lithography mask optimization, overcoming traditional ILT computational bottlenecks. Novel QUBO transformation methodology addresses high-order cost function limitations while achieving theoretical <10μs optimization speed through a quantum annealer. Use a dynamic cost function modification technique to ensure quadratic approximation accuracy. This breakthrough introduces quantum advantage to semiconductor manufacturing, pioneering a new computational lithography paradigm and establishing critical foundations for future large-scale quantum applications.

The integration of quantum annealing into computational lithography can reduce the design cycle time and substantially lower the computational overhead associated with full-chip mask correction. This is particularly important as the semiconductor industry faces escalating costs in design, verification, and mask writing for complex layouts. By achieving higher accuracy in reticle optimization, manufacturers can minimize the number of process iterations and improve pattern transfer robustness, ultimately reducing wafer production costs.