Genetic Algorithms (GA) for Process Optimization is the application of evolution-inspired search algorithms to find optimal semiconductor process recipes — maintaining a population of candidate solutions that evolve through selection, crossover, and mutation to maximize yield or minimize defects.

How GA Works for Processes

• Population: A set of candidate recipes (chromosomes), each encoding process parameters.
• Fitness: Evaluate each recipe's performance (yield, uniformity, CD) — the fitness function.
• Selection: Higher-fitness recipes are more likely to be selected as parents.
• Crossover: Combine parameters from two parent recipes to create offspring.
• Mutation: Randomly perturb some parameters to maintain diversity.

Why It Matters

• Multi-Parameter: Effectively handles 10-100+ recipe parameters simultaneously.
• Non-Linear: Finds good solutions for highly non-linear, non-convex process landscapes.
• Multi-Objective: NSGA-II and other multi-objective GAs optimize multiple quality metrics simultaneously.

GA for Process Optimization is letting recipes evolve — using natural selection principles to breed increasingly better process recipes.