The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 12, 1999
Filed:
Oct. 25, 1996
Qingsu Wang, Travis, TX (US);
Gerald Barnett, Travis, TX (US);
R Michael Greig, Travis, TX (US);
Yi Cheng, Travis, TX (US);
Advanced Micro Devices, Inc., , US;
Abstract
A system and method for detecting faults in wafer fabrication process tools by acquiring real-time process parameter signal data samples used to model the process performed by the process tool. The system includes a computer system including a DAQ device, which acquires the data samples, and a fault detector program which employs a process model program to analyze the data samples for the purpose of detecting faults. The model uses data samples in a reference database acquired from previous known good runs of the process tool. The fault detector notifies a process tool operator of any faults which occur thus potentially avoiding wafer scrap and potentially improving mean time between failures. The fault detector also receives notification of the occurrence of process events from the process tool, such as the start or end of processing a wafer, which the fault detector uses to start and stop the data acquisition, respectively. The fault detector also receives notification of the occurrence of a new process recipe and uses the recipe information to select the appropriate model for modeling the data samples. The fault detector employs a standard data exchange interface, such as DDE, between the fault detector and the model, thus facilitating modular selection of models best suited to the particular fabrication process being modeled. Embodiments are contemplated which use a UPM model, a PCA model, or a neural network model.