AI-Powered Production Scheduling
for Semiconductor Packaging
The Most Complex Scheduling Problem in Semiconductors. Solved by AI.
FabFlowAI uses multi-agent reinforcement learning to generate optimized multi-week production schedules for semiconductor packaging — adapting in real-time to tool downtime, demand shifts, and WIP changes.
Your Packaging Fab Has Thousands of Constraints. FabFlowAI Handles All of Them.
The Pain
Your fab runs thousands of lots through dozens of tool groups every week. Each lot has unique priorities, queue time windows, changeover requirements, and sequence dependencies. One wrong schedule decision cascades into tardiness, overtime, and missed customer deliveries.
The Old Way
Rule-based dispatching, heuristic scheduling, and experienced schedulers who spend days manually building weekly plans. Legacy manufacturing execution system tools re-plan slowly, can't capture your fab's true complexity, and break down when tools go offline or priorities shift.
The Fab-Flow-AI Way
Multi-agent reinforcement learning that mirrors your fab's physical layout — each AI agent optimizes its own group of stations while coordinating globally. Generates multi-week schedules in minutes. Adapts automatically when conditions change.
How It Works
1
Connect
Integrate with your manufacturing execution system or MES and ERP via connectors or REST API. FabFlowAI imports your WIP state, tool inventory, demand signals, and constraint rules.
2
Learn
Multi-agent RL builds a digital model of your specific fab — capturing every constraint, tool dependency, and queue time limit in your actual packaging flow.
3
Schedule
Output: an optimized 4–8 week production plan, updated automatically as conditions change. Plus live KPI dashboards for every station.
Packaging Operations Fab-Flow-AI Supports
Built specifically for OSAT packaging flows — not ported from wafer fab scheduling.
Metrics That Matter
5–15%
Tool utilization improvement
10–25%
Reduction in tardiness
90%
Increase in lot completion rate