genetic algorithms for process optimization

**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.