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.