Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradation, and raw material variability that can cause model performance deterioration over time. This study benchmarks machine learning operations (MLOps) retraining strategies using five years of real manufacturing data to identify optimal retraining approaches for quality prediction. We evaluate various retraining frequencies and hyperparameter optimization strategies using control limit normalized residuals as key performance metric. Results demonstrate that a fixed retraining cadence every five production batches without hyperparameter retuning achieves superior performance across all drift conditions while significantly reducing computational overhead compared to strategies incorporating hyperparameter optimization. This approach effectively maintains model accuracy during both abrupt process changes and gradual equipment degradation patterns. To address the critical need for uncertainty quantification in manufacturing decision-making, we implement conformal prediction to generate prediction confidence intervals with strong statistical guarantees. This enables proactive quality control by identifying when prediction intervals fall within acceptable control limits, transforming traditional reactive quality management into a predictive framework. The findings provide practical guidelines for implementing robust MLOps strategies in manufacturing environments where computational efficiency and reliable uncertainty quantification are paramount for operational success.

翻译：半导体材料制造中，由于工艺条件演变、设备老化和原材料变异等因素导致模型性能随时间恶化，这给机器学习部署带来了独特挑战。本研究利用五年真实制造数据，对机器学习运维（MLOps）的重新训练策略进行基准测试，以确定质量预测的最优重训练方法。我们以控制限归一化残差作为关键性能指标，评估了多种重训练频率和超参数优化策略。结果表明，采用每五个生产批次固定重训练周期且不进行超参数调优的策略，能在所有漂移条件下实现卓越性能，同时与包含超参数优化的策略相比显著降低计算开销。该方法在应对突发工艺变化和渐进式设备退化模式时均能有效维持模型精度。针对制造决策中对不确定性量化的关键需求，我们实施保形预测以生成具有强统计保证的预测置信区间。这通过识别预测区间何时落在可接受控制限内实现主动质量控制，将传统的被动质量管理转变为预测性框架。研究结果为在制造环境中实施稳健MLOps策略提供了实用指南，其中计算效率与可靠的不确定性量化对运营成功至关重要。