Semiconductors - Predicting Die-Level Failures Upstream

See how one of the world’s top chipmakers cut test costs, improved yield, and gained new QA insights with Multiscale’s MIND Platform

Executive Summary

A leading semiconductor manufacturer partnered with Multiscale Technologies to address a costly problem: identifying high-risk dies earlier in the production process—before packaging and assembly. Using interpretable AI deployed via the MIND Platform, the company was able to uncover predictive failure signals, streamline testing, and drive measurable cost savings.

The solution not only exceeded internal cost-efficiency benchmarks, it also gave engineering and QA teams the confidence to act on model-driven recommendations—shifting how they evaluate test strategy, diagnostics, and production risk.

The Challenge

With more than 10,000 test features per die and failure rates below 0.3%, the manufacturer faced steep data science hurdles:

  • High-dimensional data: 10,000+ noisy, redundant, and often incomplete measurements per die
  • Extreme class imbalance: only 0.3% of dies failed while 99.7% passed
  • Poor class separation: pass and fail samples overlapped significantly in feature space

Traditional approaches failed to deliver both precision and interpretability at scale—putting pressure on QA to rely on intuition and outdated heuristics.

Multiscale’s Solution

Multiscale implemented a hybrid modeling approach using a supervised MLP classifier and an unsupervised autoencoder, orchestrated through its modular MIND Platform.

The platform enabled rapid iteration, reproducibility, and embedded explainability—allowing engineers to move beyond black-box models. Crucially, Multiscale conducted advanced feature importance analysis, which revealed that only 200–300 features out of 7,000+ were actually predictive of downstream failures.

This insight gave the customers’ QA teams a clear, ranked list of actionable signals—many of which challenged existing assumptions and enabled smarter, leaner testing strategies.

Results

  • $50M+ in projected savings from early die filtering and reduced scrap
  • 6,000+ low-impact features pruned, streamlining test data and model complexity
  • 200–300 high-value test signals identified and prioritized
  • Testing overhead reduced while maintaining QA effectiveness
  • Models exceeded internal cost-efficiency benchmarks and are now scalable across fabs

Business Impact

The models exceeded the customer’s internal cost-efficiency benchmark, reducing unnecessary downstream processing and improving decision accuracy. Feature analysis revealed that only 200–300 out of 7,000+ test signals were predictive of failure, enabling engineers to streamline diagnostics, recalibrate QA thresholds, and eliminate low-value tests—all while maintaining product quality. Interpretability was key: by tracing predictions back to specific features, engineering and QA teams gained confidence in the results and used them to inform real-world actions.

Looking ahead, the company expects to significantly reduce scrap and rework by filtering high-risk dies earlier—contributing to an estimated $50M in cost savings. With testing previously accounting for nearly 20% of chip production costs, the new model-guided strategy is expected to reduce test volume and QA cycle time. The modeling framework, delivered through Multiscale’s MIND Platform, is now being evaluated for broader deployment across additional fabs and product lines.

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