27 citations · 54 across the 8 of their papers we have counts for
8 papers
Detecting Defective Wafers Via Modular Networks
Yifeng Zhang, Bryan Baker, Shi Chen +4
The growing availability of sensors within semiconductor manufacturing processes makes it feasible to detect defective wafers with data-driven models. Without directly measuring th…
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs
Meng Qin, Chaorui Zhang, Bo Bai +2
Many network applications can be formulated as NP-hard combinatorial optimization problems of community detection (CD). Due to the NP-hardness, to balance the CD quality and effici…
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned
Xiaoye Qian, Chao Zhang, Jaswanth Yella +3
The growing availability of the data collected from smart manufacturing is changing the paradigms of production monitoring and control. The increasing complexity and content of the…
Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer
Jaswanth Yella, Chao Zhang, Sergei Petrov +4
Over the last few decades, modern industrial processes have investigated several cost-effective methodologies to improve the productivity and yield of semiconductor manufacturing.…
GraSSNet: Graph Soft Sensing Neural Networks
Yu Huang, Chao Zhang, Jaswanth Yella +5
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obta…
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word
Chao Zhang, Jaswanth Yella, Yu Huang +4
With the rapid development of AI technology in recent years, there have been many studies with deep learning models in soft sensing area. However, the models have become more compl…