Predicting Overlay Errors Before They Happen
with AI-Powered Pattern Modeling
How a leading semiconductor company used Multiscale’s MIND Platform to anticipate nanometer-scale misalignment across complex chip designs—enabling higher yield and smarter lithography correction
Executive Summary
A leading semiconductor manufacturer partnered with Multiscale Technologies to address a persistent production challenge: intra-field overlay misalignment during fabrication. In advanced chip designs, even a 5nm deviation in pattern alignment can compromise an entire wafer. Traditional approaches flagged these issues only after wafers were printed—resulting in rework, scrap, and inconsistent yield.
To shift from reactive detection to proactive correction, the company used Multiscale’s MIND Platform to build a predictive model based solely on design pattern data. By converting layout files into structured spatial inputs and applying Fourier Neural Operators (FNO), the team developed a model capable of generalizing to unseen designs and forecasting overlay drift before fabrication.
The solution is now deployed through the company’s internal platform, delivering overlay predictions directly into fab workflows. Engineers use the model’s output to preemptively adjust print settings, reducing downstream issues and increasing line efficiency. A dedicated app was developed to scale adoption across teams and fabs.
The Challenge
Advanced chip manufacturing involves more than 1,000 precision steps—each requiring near-perfect alignment between layers. Small misalignments, known as overlay errors, were a recurring yield limiter—especially in high-density layouts. While design patterns clearly influenced overlay variation, the company lacked a way to predict that behavior in advance.
Traditional rule-based and statistical models failed to capture spatial context or generalize to new designs. The team needed a robust machine learning solution that could predict overlay misalignment before printing, without relying on downstream process data.
Multiscale’s Solution
Multiscale and the semiconductor manufacturer co-developed a machine learning pipeline to forecast overlay drift from pattern geometry alone. The workflow included:
- Conversion of layout designs into structured image-like representations
- Use of Fourier Neural Operators (FNO) to capture spatial distortions
- Generalization across unseen design IDs
- Integration into existing fab systems for real-time use
The effort progressed in two phases. Phase 1 focused on a fixed set of repeating patterns, while Phase 2 extended the model to support variable and more complex designs. The solution demonstrated consistent accuracy gains over internal baselines—enabling predictive alignment correction for even the most challenging layouts.
Results
The deployed model delivered real-world, measurable improvements:
- Overlay misalignment now predicted before printing
- Seamless integration with existing fab workflow systems
- Live use by engineers to proactively correct overlay drift
- Consistent outperformance vs. internal baselines (RMSE, min/max error)
- Developed app to scale across fabs and teams
Business Impact
By shifting from reactive detection to predictive correction, the semiconductor manufacturer now prevents alignment errors before they occur—reducing wafer scrap, minimizing rework, and improving process consistency. The solution enhances yield while giving engineers a faster, more confident path to production for each new design.
Following its success in overlay prediction, the same modeling framework is now being considered for additional process steps where spatial variation impacts yield.