Semiconductors
Predict die failures, overlay errors, and test signal issues—at fab scale.
Each chip design is unique and tailored to specific applications and customer requirements, leading to high variability in design parameters and manufacturing processes. The MIND Platform helps semiconductor engineering and data science teams model that complexity directly—turning raw test data, layout geometries, and process variation into actionable insights.
Rapid Manufacturing Innovation
Accelerate the discovery and validation of advanced semiconductor materials and device structures.
- Screen high-k dielectrics and gate oxide formulations using AI-augmented materials modeling.
- Simulate device performance and failure risks across design variants before physical fabrication.
- Integrate physics-based modeling with machine learning to shorten experimentation cycles.
Optimized Device and Test Design
Refine devices and test strategies based on signal fidelity, interpretability, and predictive power.
- Predict die-level failures early using upstream test data with thousands of variables per unit.
- Identify which test signals contribute meaningful insight—and which add unnecessary cost.
- Guide the design of transistors, memory structures, and QA flows for better performance and yield.
Pattern-Aware Process Control
Understand and control spatial variation in lithography and fab operations.
- Predict overlay misalignment directly from layout geometry using image-based AI models.
- Generalize across unseen DESIGN_IDs to support dynamic product and mask schedules.
- Integrate predictions into fab systems for real-time process corrections before defects occur.
Enhanced Manufacturing Processes
Analyze real-time sensor and inspection data to detect anomalies and reduce unplanned downtime.
- Use anomaly detection and failure classification models to surface emerging process drift.
- Enable root cause analysis and alerting based on pattern deviation and reconstruction error.
- Support continuous model learning with version control, interpretability, and human review.
End-to-End Optimization Across the Value Chain
From material R&D to test optimization and inline process control, MIND supports scalable AI deployment across the entire semiconductor lifecycle.
- Run consistent workflows across fabs, device families, and engineering teams.
- Ensure models meet both accuracy and cost-efficiency benchmarks.
- Deploy interpretable models that earn trust from process, test, and yield engineers.
Solving Hard Problems Starts Here.
Let’s solve yours next. From simulation bottlenecks to multiscale manufacturing complexity—tell us what you’re up against. We’ll dig in together.
