Lam Research Corporation

Raymond Chau

Vincent M Wong

Chung-Ho Huang

Henry T Chan

Ngoc-Diep Nguyen

Scott Baldwin

Rainer Unterguggenberger

Gerramine S Manuguid

Bridget Hill

Yu Ding

Thomas A Stubblebine

A substrate processing system comprises a module to perform an operation associated with processing a semiconductor substrate in the substrate processing system. The module includes a component used with the processing of the semiconductor substrate, and a file stored in the module. The file includes information about the component of the module. The substrate processing system comprises a controller to communicate with the module via a network of the substrate processing system. The controller receives the file from the module via the network, reads the information about the component from the received file, and maps, based on the information read from the received file, the component of the module to an option in an application used to configure the module. The controller automatically configures the component of the module using the option in the application to which the component of the module is mapped.

Autoconfiguration of hardware components of various modules of a substrate processing tool

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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San Ramon, CA, United States of America

Raymond Chau