Design of an Integrated Method for Optimizing Electron Beam Lithography Using Genetic Algorithms, Particle Swarm Optimization, and Grey Wolf Optimizer

With the ever-increasing demands of advanced manufacturing for precise micro-and nanoscale patterning, optimization of the EBL process is in urgent need. The current optimization methods involve combinations such as GA with GWO or PSO with GWO, which are plagued by poor exploration-exploitation trade-offs and hence converge to suboptimal solutions or insufficient refinement of the solution. The above challenges are overcome by an innovative Adaptive Wolf-Driven Swarm Evolution approach, synergizing the strengths of GA, PSO, and GWO for the optimization process of EBL. The generation of a diversified population of solutions from GA is the starting of AWDSE ensuring wide explorations in the search space. Further, a role-based classification using GWO classifies solutions hierarchically into different roles: Alpha, Beta, Gamma, Delta. Solutions at higher ranks (Alpha, Beta) are then refined by PSO-based updates that exploit the search space by updating the solutions, while the solutions ranked lower (Gamma, Delta) are subjected to GA-driven crossover and mutation operations to maintain diversity and exploration. The adaptive switching between the evolutionary operations of GA and particle updates of PSO may definitely be driven by the leadership dynamics of GWO, which allows more intensive balancing of diversification-intensification, thus allowing improved convergence precision and velocity. Experimental results prove that AWDSE is able to obtain an improvement of about 18% reduction of critical dimensions with a shrinkage in delay time amounting to 12%, outperforming traditional approaches like GA-GWO and PSO-GWO. This advance underlines the possibility of AWDSE to significantly enhance EBL efficiency and accuracy, with vistas toward ever finer and faster ways of nanomanufacturing process.