TS-HINT: Enhancing Semiconductor Time Series Regression Using Attention Hints From Large Language Model Reasoning
This work improves semiconductor manufacturing process modeling, but it is incremental as it builds on existing time series foundation models with attention mechanisms.
The paper tackles the problem of predicting material removal rate in semiconductor manufacturing by addressing the loss of temporal dynamics and data inefficiency in existing methods, achieving effective few-shot learning directly from multivariate time series features.
Existing data-driven methods rely on the extraction of static features from time series to approximate the material removal rate (MRR) of semiconductor manufacturing processes such as chemical mechanical polishing (CMP). However, this leads to a loss of temporal dynamics. Moreover, these methods require a large amount of data for effective training. In this paper, we propose TS-Hint, a Time Series Foundation Model (TSFM) framework, integrated with chain-of-thought reasoning which provides attention hints during training based on attention mechanism data and saliency data. Experimental results demonstrate the effectiveness of our model in limited data settings via few-shot learning and can learn directly from multivariate time series features.