Stochastic Optimization is a class of optimization methods that incorporate randomness in the search process or account for randomness in the objective function — using probabilistic elements to escape local optima, handle noisy evaluations, and explore large, complex parameter spaces common in semiconductor manufacturing.

Key Stochastic Methods

• Genetic Algorithms: Population-based evolution with selection, crossover, and mutation.
• Simulated Annealing: Random perturbations with temperature-controlled acceptance probability.
• Particle Swarm: Particles explore the space guided by personal and global best solutions.
• Bayesian Optimization: Probabilistic surrogate model guides efficient exploration of expensive functions.

Why It Matters

• Global Optima: Stochastic methods can escape local optima that trap deterministic gradient methods.
• Noisy Functions: Naturally handle noisy, stochastic objective functions (yield, process variability).
• No Gradient Needed: Work with black-box functions where gradients are unavailable.

Stochastic Optimization is organized randomness for finding the best — using controlled randomness to optimize complex, noisy manufacturing processes.