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<p>Accurate prediction of cycle time (CT) is crucial for ensuring the timely delivery of wafer products, considering the inherent uncertainties in wafer fabrication systems. However, the concept drift problem arises when types of wafer products change due to the dynamic introduction of new orders, causing variations in the joint probability distribution of data. This drift challenges the performance of existing CT prediction models based on batch learning, potentially leading to degraded or failed predictions. To tackle this challenge, we propose a continual deep learning prediction method that continuously learns knowledge regarding new types of wafer products by memorizing and updating the wafer data stream. Specifically, we employ model-based concept drift detection on the incoming data flows and devise a dynamic sampling strategy to balance the sample distribution between minority and majority classes, thereby addressing the class imbalance problem. Within the continual learning framework, our proposed FD-MIR (Feature Distillation with Maximally Interfered Retrieval) method integrates a teacher-student feature distillation architecture and a Clustering-driven Adaptive Threshold Maximally Interfered Retrieval (CA-MIR) strategy with adaptive K selection: when concept drift is detected, the pre-drift model is frozen as the teacher to guide feature learning of the student model, while the CA-MIR dynamically determines the number of critical historical samples (K) for replay through clustering analysis, by clustering interference values into multiple clusters and selecting samples based on a cumulative interference ratio threshold. Experimental results demonstrate the superior performance of our method in accurately predicting CT under scenarios where the types of wafer products change. The proposed method holds great potential for enhancing the efficiency and effectiveness of CT prediction in wafer manufacturing systems.</p>

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Adaptive continual learning for cycle time prediction in wafer fabrication with time-varying product types

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Peng Zhang,
  • Hongsen Li,
  • Ming Wang,
  • Yufeng Wu,
  • Junliang Wang,
  • Jie Zhang

摘要

Accurate prediction of cycle time (CT) is crucial for ensuring the timely delivery of wafer products, considering the inherent uncertainties in wafer fabrication systems. However, the concept drift problem arises when types of wafer products change due to the dynamic introduction of new orders, causing variations in the joint probability distribution of data. This drift challenges the performance of existing CT prediction models based on batch learning, potentially leading to degraded or failed predictions. To tackle this challenge, we propose a continual deep learning prediction method that continuously learns knowledge regarding new types of wafer products by memorizing and updating the wafer data stream. Specifically, we employ model-based concept drift detection on the incoming data flows and devise a dynamic sampling strategy to balance the sample distribution between minority and majority classes, thereby addressing the class imbalance problem. Within the continual learning framework, our proposed FD-MIR (Feature Distillation with Maximally Interfered Retrieval) method integrates a teacher-student feature distillation architecture and a Clustering-driven Adaptive Threshold Maximally Interfered Retrieval (CA-MIR) strategy with adaptive K selection: when concept drift is detected, the pre-drift model is frozen as the teacher to guide feature learning of the student model, while the CA-MIR dynamically determines the number of critical historical samples (K) for replay through clustering analysis, by clustering interference values into multiple clusters and selecting samples based on a cumulative interference ratio threshold. Experimental results demonstrate the superior performance of our method in accurately predicting CT under scenarios where the types of wafer products change. The proposed method holds great potential for enhancing the efficiency and effectiveness of CT prediction in wafer manufacturing systems.