Abstract

Semiconductor lithography is a complex cyber-physical system where timely integration of heterogeneous operational data and informed decision-making are critical to take preventive action. Data-driven analytics can predict equipment degradation, but due to poor information pipelines, complex decision logic, and a lack of human expertise integration, it can be difficult to put it into practice. This study aims to produce an integrated framework of industrial informatics for explainable, human-in-the-loop, predictive action in semiconductor lithography using sensor telemetry, maintenance records, and text analytics together with transformer-based approaches and SHAP explainability. The framework is tested with existing industrial lithography data from various facilities, collected in the past under different experimental conditions. Three experiments are conducted to evaluate the predictive performance (LSTM vs. Transformer models), monitoring behavior (anomaly detection using autoencoders), and human–AI collaboration with explainability. Evaluation looks at not only outcomes of human–AI interaction, but also predictive performance and monitoring behavior—not deployment and not causation. Transformer models yield 97.2% accuracy compared to 94.8% for the LSTM baseline (+2.4 pp). Anomaly detection results in a nominal 3.2 pp difference (Autoencoder 94.5% vs. Random Forest 91.3%; p=0.018, uncorrected). The integration of human-in-the-loop raises the operator’s trust from 62% to 92% (p<0.001) and decision acceptability from 70% to 92%. Under retrospective counterfactual evaluation, operational metrics estimate a 68% reduction in downtime, a 65.1% reduction in yield loss, and a 36% reduction in maintenance costs.

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