Attention-Based Deep Learning for Multi-Target Prediction of Process Time and Tool Availability in Large-Scale Semiconductor Fabs

Abstract

Precise prediction of processing time (PT) and machine availability is critical for optimizing fabrication throughput. This paper presents a generalized, attention-based deep neural network designed to capture non-linear tool dynamics and high-dimensional categorical dependencies. Unlike rigid statistical baselines, our framework adapts to multiple prediction targets, including equipment-level process completion and granular chamber-level availability. Deployed in multiple GlobalFoundries semiconductor fabrication plants, the model demonstrates a 50% to 80% reduction in mean absolute error (MAE) compared to historical averages. Production results confirm a 2% to 3% increase in moves per productive hour and a 3% to 7% sustained increase in chamber utilization across a majority of the tools that we tested with. These results validate the strength of machine learning methods in enabling advanced parallel loading and just-in-time dispatching strategies.