Traditional wafer manufacturing yield prediction relies on engineers' experience, resulting in slow, reactive responses that cause costly production downtime. To overcome these limitations, a new intelligent yield prediction system (part of the YES end-to-end platform) leverages large language models and advanced algorithms to proactively forecast yields, quantify losses, pinpoint root causes, and guide recovery strategies.
Offers the following core advantages. Real-Time AI Prediction & Alerts that engineers can query yield status via natural language dialogue. The system instantly forecasts final yield loss and automatically triggers alerts when metrics exceed preset thresholds, enabling early intervention before defects escalate.
Automated Root Cause Analysis predictions are directly linked to multi-source data (process logs, FDC, historical tool performance). Using correlation analysis, the system identifies top root causes in ~24 minutes, slashing traditional analysis time from hours.
Closed-Loop Continuous Optimization of every resolved anomaly feeds back into the AI model, dynamically refining prediction algorithms and knowledge bases. This creates a self-improving cycle that shifts yield management from passive response to proactive defense.
In a real-world demonstration, engineers used natural language queries to trace a yield loss spike to insufficient CVD equipment cleaning, quickly confirming the root cause and initiating targeted monitoring. Rather than just outputting numbers, the system builds a "questionable, traceable, modifiable, and evolvable" intelligent workflow. It transforms subjective experience into quantifiable, data-driven decisions, significantly minimizing downtime, accelerating yield recovery, and maximizing manufacturing efficiency in semiconductor production.
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