  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.
