The Technology Behind FabFlowAI
Multi-agent reinforcement learning.
Packaging-specific constraint modeling.
Kubernetes-native deployment.
Multi-Agent Reinforcement Learning Architecture
FabFlowAI's core engine is built on multi-agent reinforcement learning. Each group of stations pertaining to an operation in your fab has a dedicated AI agent that learns the optimal decisions for that operation. Agents coordinate globally through a shared communication layer, ensuring system-level objectives (minimize tardiness, maximize throughput, reduce changeovers) are met. Leader agents provide goals and distribute reward to subagents.
This architecture mirrors the physical reality of semiconductor packaging — where each tool group operates semi-independently but is tightly coupled through lot travel, WIP and changeover constraints, and queue time windows. Traditional centralized scheduling approaches can not scale to this complexity; the decentralized multi-agent approach handles it naturally.
How the Agents Work
- State space: Each agent observes the full fab state — WIP levels, lot priorities, tool availability, changeover state, queue time elapsed
- Action space: Which lot to process next on each available station, when to schedule a changeover, whether to expedite a lot
- Reward function: Multi-objective: tardiness minimization + throughput maximization + changeover reduction + idle time minimization
- Intrinsic reward of meeting goals
- Training: Simulation-based training on historical fab data, then online adaptation as conditions change
Kubernetes-Native Infrastructure
FabFlowAI deploys as a set of containerized microservices in your existing Kubernetes infrastructure or in a cloud. No data leaves your facility if you choose so. The entire platform runs on-prem, in your private cloud, or on an external cloud provider.
- Containerized microservices for horizontal scalability
- Ray distributed computing for training and inference enables limitless scalability
- Runs in your fab's existing Kubernetes cluster or external cloud
- REST API for MES/ERP integration
- On-prem, private cloud deployment or public cloud
- No data ever transmitted outside your facility if required
Simulation Before Commitment
Before any schedule is pushed to the floor, FabFlowAI runs it through a digital twin of your fab. This lets schedulers and plant managers validate the plan, test "what-if" scenarios, and make informed decisions — without risking production.
Security & Deployment
- Fully on-prem deployment if required — no data leaves your facility
- Role-based access control throughout the platform
- Kubernetes-native: scales horizontally as your fab grows by relying on Kubernetes and Ray
- ISO 27001 / SOC 2 ready architecture