用大模型推理生成注意力提示，提升小样本下半导体加工预测精度
TS-HINT: Enhancing Semiconductor Time Series Regression Using Attention Hints From Large Language Model Reasoning
- 引入大模型链式思考生成注意力提示，捕捉时间序列动态特征
- 在少样本条件下仍能准确预测材料去除率，无需大量训练数据
- 适合半导体制造中数据稀缺场景的时序回归任务
现有数据驱动方法依赖从时序数据中提取静态特征来近似半导体制造过程（如化学机械抛光）中的材料去除率（MRR），但会丢失时间动态信息，且需大量数据进行有效训练。本文提出TS-Hint，一种集成链式思考推理的时序基础模型（TSFM）框架，在训练中基于注意力机制数据与显著性数据提供注意力提示。实验表明，该模型在少样本学习设置下表现优异，可直接从多变量时序特征中学习。
原文摘要 · Abstract (English)
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.
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