# Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

arXiv: https://arxiv.org/abs/2301.08974 · HTML: https://mlsanity.com/paper/2301.08974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 21 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word](https://mlsanity.com/paper/2111.05973/) (2021)
- [GraSSNet: Graph Soft Sensing Neural Networks](https://mlsanity.com/paper/2111.06980/) (2021)
- [Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer](https://mlsanity.com/paper/2111.06981/) (2021)
- [Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned](https://mlsanity.com/paper/2111.06982/) (2021)

## Abstract (arXiv)

The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors. Effective inspection and metrology are necessary to improve product yield, increase product quality and reduce costs. In recent years, many semiconductor manufacturing equipments are equipped with sensors to facilitate real-time monitoring of the production process. These production-state and equipment-state sensor data provide an opportunity to practice machine-learning technologies in various domains, such as anomaly/fault detection, maintenance scheduling, quality prediction, etc. In this work, we focus on the task of soft sensing regression, which uses sensor data to predict impending inspection measurements that used to be measured in wafer inspection and metrology systems. We proposed an LSTM-based regressor and designed two loss functions for model training. Although engineers may look at our prediction errors in a subjective manner, a new piece-wise evaluation metric was proposed for assessing model accuracy in a mathematical way. The experimental results demonstrated that the proposed model can achieve accurate and early prediction of various types of inspections in complicated manufacturing processes.