Agent-Based Modeling (ABM) for semiconductor manufacturing is a bottom-up simulation paradigm where individual entities (agents) follow local rules — with system-level behavior emerging from the interactions between thousands of agents representing wafers, tools, operators, and controllers.

ABM vs. Traditional Simulation

• Bottom-Up: Define rules for individual agents — system behavior emerges (vs. top-down equations).
• Heterogeneity: Each agent can have unique properties (different recipes, priorities, tool states).
• Adaptation: Agents can learn and adapt their behavior based on experience.
• Spatial: Agents can be embedded in physical space (fab layout, AMHS tracks).

Why It Matters

• Complex Interactions: Captures tool-lot-operator interactions that analytical models cannot represent.
• Decentralized Decision Making: Models real fab operations where decisions are made locally, not centrally.
• Disruption Modeling: Naturally handles disruptions (tool failures, hot lots) through agent-level responses.

ABM is the microscopic view of fab dynamics — simulating every individual entity's behavior to understand how complex factory patterns emerge.