TwinFab offers a digital‑twin platform for semiconductor fabs and cleanrooms that fuses physics‑based CFD models, AI inference, and live sensor streams to produce continuous zone‑level risk maps and virtual sensor outputs. The solution provides predictive maintenance for filtration, what‑if airflow and layout simulations, and delivers actionable insights through configurable dashboards and open APIs that integrate with existing MES and control systems.
Funding
Funding not disclosed
Founders
Product
Problem
Semiconductor fabs and advanced manufacturing cleanrooms rely on sparse physical sensors and periodic manual audits, creating blind zones where contamination and filtration drift develop unnoticed. This delayed detection leads to yield loss, unplanned downtime, and suboptimal energy usage. Existing control loops provide only lagging alarms after defects have already impacted production.
Solution
TwinFab delivers an operational digital twin that continuously fuses physics‑grade simulation, AI inference, and live sensor streams into a synchronized virtual replica of the cleanroom environment. The platform generates zone‑level risk maps and virtual sensor outputs, giving operators instant visibility into conditions that physical gauges cannot capture. Predictive analytics forecast contamination and filter degradation, enabling proactive maintenance scheduling and safe operating ranges. Engineers can run validated what‑if simulations of fan, airflow, and layout changes before implementing them on the line, reducing trial‑and‑error cycles. All insights are delivered via configurable dashboards and RESTful APIs that integrate with existing Manufacturing Execution Systems (MES) and control software, turning complex dynamics into actionable recommendations.
Target Audience
Primary customers are semiconductor fab process engineers, cleanroom operations managers, and filtration equipment OEMs seeking real‑time operational intelligence for yield protection and downtime reduction.
Features
- Physics‑based CFD and particle‑transport models combined with machine‑learning surrogates for near‑real‑time inference
- Real‑time data ingestion pipeline that normalizes and streams sensor data into the digital twin with sub‑second latency
- Virtual sensing layer that extrapolates measurements to unmapped zones, producing continuous risk maps across the cleanroom
- Predictive maintenance engine that predicts filter performance decay and recommends optimal replacement windows
- What‑if simulation workspace allowing rapid evaluation of fan speed, airflow pattern, and layout modifications with quantified impact on yield and energy consumption
- Interactive web dashboard and customizable alert system delivering risk scores, trend analytics, and actionable recommendations
- Open API (REST/GraphQL) and FHIR‑compatible endpoints for seamless integration with MES, SCADA, and EDA tools
- Role‑based access control and end‑to‑end encryption to ensure data integrity and compliance with industry security standards