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Proceedings Paper

Monitoring of a RTA process using multi-PCA
Author(s): Michael L. Miller; Qingsu Wang; Terrence Riley
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Paper Abstract

Process faults usually lead to changes in the normal relationship among process variables. These changes can be detected by a principle component analysis (PCA) model based on the data from normal batches of operation. Therefore, monitoring process variables via a PCA model may lead to the earlier detection of process fault than traditional SPC method which depends on periodic information from test wafers.However, PCA is a linear method, and does not explain the relationship among process variables with time. Because the relationship among process variables for a wafer processing equipment is both nonlinear, applying PCA method directly to the monitoring of such processes may prove to be difficult. A multi-PCA modeling technique is proposed in our study of process in our study of process monitoring and fault detection for a semiconductor manufacturing tool. A series of local PCA models can be built with each local model only describing a local relationship among process variables at a particular time.During monitoring, if an observation from the kth sampling time of previous normal runs, this observation can be considered normal. A method is also proposed to eliminate redundant local PCA models. The proposed method has been implemented for the monitored of a commercial Rapid Thermal Anneal (RTA) tool. The RTA process is a typical single wafer process. For the same product, all wafers should be processed according to the same recipe. First, the data from normal lots were collected and verified by wafer electrical test data to be normal data. A multi-PCA model was built based on all data for these normal production lots. In the modeling, only 2 or 3 principal components were necessary for each local PCA model to explain 99 percent variance of each sub-matrix of data. This paper will discuss using Multi-PCA as a modeling method for detecting real-time process variations based on equipment signals, with abnormal process signals being indicated by a single parameter - Squared Prediction Error - from the PCA mode. Issues related to the use of this technique in a state-of-the-art semiconductor fab will also be discussed.

Paper Details

Date Published: 3 September 1998
PDF: 7 pages
Proc. SPIE 3507, Process, Equipment, and Materials Control in Integrated Circuit Manufacturing IV, (3 September 1998); doi: 10.1117/12.324365
Show Author Affiliations
Michael L. Miller, Advanced Micro Devices, Inc. (United States)
Qingsu Wang, Advanced Micro Devices, Inc. (United States)
Terrence Riley, Advanced Micro Devices, Inc. (United States)


Published in SPIE Proceedings Vol. 3507:
Process, Equipment, and Materials Control in Integrated Circuit Manufacturing IV
Anthony J. Toprac; Kim Dang, Editor(s)

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