ALDTwin – A Hybrid Physics-Based and Data-Driven Digital Twin of Atomic Layer Deposition Process for Semiconductor Innovation and Manufacturing

Atomic layer deposition (ALD) is essential for conformal thin-film growth in advanced semiconductor fabrication, where equipment monitoring and maintenance are critical for atomic-scale precision and process consistency. However, in shared cleanroom environment, monitoring equipment health and predicting process outcomes are challenging because the same tools support diverse recipes, users, materials and operating conditions, while reliable fault and maintenance labels are scarce. We present ALDTwin, a hybrid physics-based and data-driven digital twin with an LLM agent-coordinated workflow for monitoring ALD processes under weakly labeled conditions. The methodology distinguishes intentional recipe-induced chamber conditioning effects from unintended equipment-state changes, including drift, contamination and malfunctions. We demonstrate ALDTwin using sensor data from a shared lab-scale cleanroom ALD system. The LLM agent coordinates data preparation and qualification before invoking simulation and anomaly models. Validated sensor logs are transformed into process-relevant features, after which an authoritative monitoring layer evaluates tool state, run completeness and data quality. Forty-four process metrics are compared with adaptive recipe-specific baselines to suppress expected recipe-dependent variation and identify equipment-state deviations. Qualified runs are passed to a physics-based feature scale simulation for estimating deposition outcomes, while incomplete or abnormal runs bypass simulation but remain part of longitudinal tool-state assessment, with abnormal runs escalated for engineer inspection. In parallel, one LSTM autoencoder detects abrupt deviations within multivariate sensor traces, while a second identifies gradual equipment drift across successive runs using process-relevant metrics. Finalized metric-based anomaly scores are incorporated into a Kalman forecasting layer to project near-term tool behavior. By separating probabilistic LLM coordination from deterministic qualification and physics-based evaluation, ALDTwin integrates safeguards, unsupervised learning, simulation and temporal forecasting within an interpretable, grounded, self-verifying framework for trustworthy process monitoring and decision support.