Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing
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Abstract (arXiv)
Semiconductor manufacturing relies on tightly interconnected components, so early identification of the assets most likely to fail is essential to prevent a single breakdown from disrupting the entire production pipeline. Maintenance planning must therefore balance unexpected failures against prematurely interrupted operating life. We present a Predictive Maintenance (PdM) framework combining Deep Learning (DL) sequence models and Simoultaneous Quantile Regression (SQR) for uncertainty-aware Remaining Useful Life (RUL) estimation and risk-aware maintenance decisions. Several architectures are compared on ion-milling data from the 2018 PHM Data Challenge (PHM18), including architectures based on State Space Models (SSM), using prediction and business metrics: Unexpected Breaks (UB), Unexploited Lifetime (UL), and a cost-weighted objective. Diagonal State Spaces (S4D) delivers the best Remaining Useful Life (RUL) estimates across quantiles and, relative to Preventive Maintenance (PvM) baselines, substantially lowers business cost by avoiding systematically early interventions. The results support uncertainty-aware, cost-sensitive maintenance planning in semiconductor production.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 35 references that are in the map. First posted on arXiv 26 Aug 2026.
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References · 7
- 2017Attention Is All You Needcited by 60,361
- 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
- 2022Simplified State Space Layers for Sequence Modelingcited by 788
- 2022Diagonal State Spaces are as Effective as Structured State Spacescited by 350
- 2020Fusing Physics-based and Deep Learning Models for Prognosticscited by 40
- 2024Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descentcited by 1
- 2025A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Modelscited by 1
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- 2025Optimizing Predictive Maintenance in Intelligent Manufacturing: An Integrated FNO-DAE-GNN-PPO MDP Framework
- 2021An Offline Deep Reinforcement Learning for Maintenance Decision-Making
- 2026Distributional Reinforcement Learning for Condition-Based Maintenance of Multi-Pump Equipment
- 2025Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights
- 2026Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations
- 2026Planning or Learning: Reliability and Cost in Multi-Asset Maintenance
- 2024Maintenance Optimization for Asset Networks with Unknown Degradation Parameters ↗
- 2025Learning to Optimize Capacity Planning in Semiconductor Manufacturing