Lam Research Corporation

Gerramine S Manuguid

Shantinath Ghongadi

Vincent M Wong

Chung-Ho Huang

Robert Marshall Stowell

Henry T Chan

Quan Ma

Ngoc-Diep Nguyen

Raymond Chau

Yu Ding

In one embodiment, the disclosed apparatus is an in-situ, closed-loop bubble and foam detection and reduction system that includes a liquid-level sensor to determine a volume of a liquid in a fluid reservoir, a mass-detection device to determine a mass of the fluid reservoir and any liquid contained within the fluid reservoir, a processor electrically coupled to the liquid-level sensor and the mass-detection device to determine an actual volume of the liquid within the fluid reservoir, and a showerhead coupled to the processor and positioned above the fluid reservoir. The showerhead is activated by the processor when a volume of the liquid determined by the liquid-level sensor exceeds the actual volume of the liquid by a predetermined amount. Other apparatuses and methods are disclosed.

In-situ sensor-fusion with artificial intelligence

For etching tools, a neural network model is trained to predict optimum scheduling parameter values. The model is trained using data collected from preventive maintenance operations, recipe times, and wafer-less auto clean times as inputs. The model is used to capture underlying relationships between scheduling parameter values and various wafer processing scenarios to make predictions. Additionally, in tools used for multiple parallel material deposition processes, a nested neural network based model is trained using machine learning. The model is initially designed and trained offline using simulated data and then trained online using real tool data for predicting wafer routing path and scheduling. The model improves accuracy of scheduler pacing and achieves highest tool/fleet utilization, shortest wait times, and fastest throughput.

Model-based scheduling for substrate processing systems

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Lake Oswego, OR, United States of America

Gerramine S Manuguid