Using large language models to enhance the reusability of sensor data
The Internet of Things generates vast data volumes via diverse sensors, yet its potential
remains unexploited for innovative data-driven products and services. Limitations arise from …
remains unexploited for innovative data-driven products and services. Limitations arise from …
Grassnet: Graph soft sensing neural networks
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 …
smart manufacturing to guide production and optimize inspection. The industrial data …
Data-Driven Machine Learning Predictor Model for Optimal Operation of a Thermal Atomic Layer Etching Reactor
As semiconductor devices continue to shrink to nanoscale dimensions and take on
increasingly complex geometries, novel fabrication processes and techniques must emerge …
increasingly complex geometries, novel fabrication processes and techniques must emerge …
Soft-sensing conformer: A curriculum learning-based convolutional transformer
Over the last few decades, modern industrial processes have investigated several cost-
effective methodologies to improve the productivity and yield of semiconductor …
effective methodologies to improve the productivity and yield of semiconductor …
Soft sensing model visualization: Fine-tuning neural network from what model learned
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 …
paradigms of production monitoring and control. The increasing complexity and content of …
Real-Time Vertical Path Planning Using Model Predictive Control for an Autonomous Marine Current Turbine
This paper presents a predictive approach to address real-time vertical path planning for a
marine current turbine (MCT) treated as an autonomous underwater vehicle (AUV), where …
marine current turbine (MCT) treated as an autonomous underwater vehicle (AUV), where …
Soft-Sensing Regression Model: From Sensor to Wafer Metrology Forecasting
A Fan, Y Huang, F Xu, S Bom - Sensors, 2023 - mdpi.com
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 …
market sectors. Effective inspection and metrology are necessary to improve product yield …
[HTML][HTML] Integration of on-line machine learning-based endpoint control and run-to-run control for an atomic layer etching process
H Wang, F Ou, J Suherman, G Orkoulas… - Digital Chemical …, 2024 - Elsevier
Abstract Control methods for Atomic Layer Etching (ALE) processes are constantly evolving
due to the increasing level of precision needed to manufacture next-gen semiconductor …
due to the increasing level of precision needed to manufacture next-gen semiconductor …
[HTML][HTML] Industrial data-driven machine learning soft sensing for optimal operation of etching tools
Abstract Smart Manufacturing, or Industry 4.0, has gained significant attention in recent
decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As …
decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As …
Detecting Defective Wafers Via Modular Networks
Y Zhang, B Baker, S Chen, C Zhang, Y Huang… - arXiv preprint arXiv …, 2025 - arxiv.org
The growing availability of sensors within semiconductor manufacturing processes makes it
feasible to detect defective wafers with data-driven models. Without directly measuring the …
feasible to detect defective wafers with data-driven models. Without directly measuring the …