# Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing

arXiv: https://arxiv.org/abs/2609.22160 · HTML: https://mlsanity.com/paper/2609.22160/

## 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.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Diagonal State Spaces are as Effective as Structured State Spaces](https://mlsanity.com/paper/2203.14343/) (2022)
- [Fusing Physics-based and Deep Learning Models for Prognostics](https://mlsanity.com/paper/2003.00732/) (2020)
- [Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent](https://mlsanity.com/paper/2402.01098/) (2024)
- [A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Models](https://mlsanity.com/paper/2506.17018/) (2025)

## 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.