Disentangling Prognostic Observability from Policy Stochasticity in PHM-Aware Reinforcement Learning for Semiconductor Fab Dispatch
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Published
Sep 28, 2026
Sean Mondesire
Tori Wright Bulent Soykan
https://orcid.org/0000-0002-5152-1709
Tori Wright Bulent Soykan
Abstract
High-mix low-volume (HMLV) semiconductor manufacturing is a demanding proving ground for prognostics and health management (PHM): frequent product changeovers, re-entrant routes, and heavy tool-qualification overhead amplify the operational cost of unexpected failures, so healthaware dispatch justify its sensing investment. This work investigates that promise in a reliability-aware HMLV fab simulator using a 2×2 ablation of two deep reinforcement learning proximal policy optimization (PPO) dispatchers (PHMaware vs. dispatch-only) and two deployment modes (deterministic argmax vs. stochastic sampling), all sharing reward, training budget, and seeds. Across 10 seeds and 10 heldout HMLV order sets, deployment mode dominated: stochastic PHM-aware PPO cut mean makespan from 48,939.9 to 24,387.2 minutes and lifted OEE from 0.707 to 0.829 (all FDR-significant), while PHM-aware and dispatch-only PPO were statistically indistinguishable under matched deployment. The result sharpens the PHM-awareness case for HMLV fabs: prognostic value is real but contingent on how learned policies turn information into action.
How to Cite
Mondesire, S., Wright, T., & Soykan, B. (2026). Disentangling Prognostic Observability from Policy Stochasticity in PHM-Aware Reinforcement Learning for Semiconductor Fab Dispatch. Annual Conference of the PHM Society, 18(1). https://doi.org/10.36001/phmconf.2026.v18i1.4907
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Keywords
semiconductor manufacturing, reinforcement learning, policy stochasticity, prognostic health, deep learning
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10.1016/j.simpat.2018.08.005
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scheduling for job shop optimization. In Proceedings
of the winter simulation conference (wsc).
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10.3390/electronics12030706 doi:
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