Solution

Start with one module or deploy end-to-end across your cleanroom environment.

The Challenge

Risk is often invisible until it impacts yield. Traditional audits and periodic studies are slow, and alerts can arrive too late.

TwinFab Approach

A live operational digital twin that fuses sensor data + physics + AI to monitor, predict, and recommend actions continuously.

Contamination Prevention

Early detection + prescriptive actions to prevent excursions.

Predictive Maintenance

Health forecasting for filters and critical equipment to reduce surprises.

Operational Optimization

Run “what-if” scenarios to tune airflow and filtration for efficiency.

Ecosystem Enablement

Value for fabs and filtration OEMs—monitoring, validation, and improvement.

Why traditional control methods fall short

Periodic audits, fixed replacement schedules, and lagging indicators often detect problems after damage is done. TwinFab shifts teams from reactive response to predictive control—powered by an operational digital twin.

  • Reduce blind zones: infer conditions between sensors using physics + AI.
  • Plan maintenance: forecast filter health and validate replacement timing.
  • Validate changes safely: compare “what-if” settings before deployment.
Traditional control methods are slow and reactive
Living operational digital twin infographic

Deploy as modules—or end-to-end

Start with contamination visibility, filter health forecasting, or scenario simulation. Expand over time into a unified live operations layer across the cleanroom.

Visibility

Zone risk maps, virtual sensors, and anomaly detection.

Prediction

Excursion risk, yield impact trends, and early warnings.

Optimization

Energy-efficient airflow, setpoint tuning, and validation.

Technology

Hybrid physics + AI engine powering real-time digital twins.

TwinFab blends validated physics with fast AI surrogates to keep a high-fidelity twin synchronized with reality—then turns that into predictions, alerts, and what-if analysis.

Select a block

Click any architecture block to see a short explanation.

Physics-Informed AI

Reduced-order models capture airflow and contaminant behavior with near real-time performance.

Live Data Integration

Ingest sensor, SCADA, and MES signals to align the twin and create “virtual sensors” between gauges.

Security-First Platform

Designed for enterprise deployments with security-first architecture and integration-friendly APIs.

Live Fab Operations Center

Experience instant visibility and predictive control. Adjust the fab parameters to see how the digital twin surfaces risk hotspots and optimization tradeoffs.

Fab Parameters

65%

Lower speed saves energy but can increase contamination risk.

98%

Simulate aging filters to test predictive alerts.

35

Illustrative range used in AMC filter validation demos.

180

Higher flow can change breakthrough timing and outlet behavior.

TwinFab AI Mode
Auto-optimize settings
Energy Savings
42%
Yield Risk
Warning

Real-time Contamination Map

Optimal Warning Critical

The digital twin estimates airflow stagnation points and particle accumulation (Warning/Critical zones) based on your settings.

Virtual Sensors
Outlet (ppb)12
Stagnation Index0.42
Breakthrough (min)38

Airborne Particle Count (0.5μm)

Outlet Concentration Validation (ppb)

Test data TwinFab

Projected Yield Loss Trend

Standard Fab With TwinFab

Calculate Your Potential Impact

Estimate the benefit of reducing contamination-related yield loss through earlier detection and intervention.

1.4%
Illustrative only. Adjust to match your baseline assumptions.

This calculator is for estimation. It does not include pricing and is not investment material.

Potential Annual Savings
$5.2M
Based on reducing contamination-related yield loss by 50% through early detection and intervention.