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Proceedings Paper

Recursive least squares estimation and its application to shallow trench isolation
Author(s): Jin Wang; S. Joe Qin; Christopher A. Bode; Matthew A. Purdy
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Paper Abstract

In recent years, run-to-run (R2R) control technology has received tremendous interest in semiconductor manufacturing. One class of widely used run-to-run controllers is based on the exponentially weighted moving average (EWMA) statistics to estimate process deviations. Using an EWMA filter to smooth the control action on a linear process has been shown to provide good results in a number of applications. However, for a process with severe drifts, the EWMA controller is insufficient even when large weights are used. This problem becomes more severe when there is measurement delay, which is almost inevitable in semiconductor industry. In order to control drifting processes, a predictor-corrector controller (PCC) and a double EWMA controller have been developed. Chen and Guo (2001) show that both PCC and double-EWMA controller are in effect Integral-double-Integral (I-II) controllers, which are able to control drifting processes. However, since offset is often within the noise of the process, the second integrator can actually cause jittering. Besides, tuning the second filter is not as intuitive as a single EWMA filter. In this work, we look at an alternative way Recursive Least Squares (RLS), to estimate and control the drifting process. EWMA and double-EWMA are shown to be the least squares estimate for locally constant mean model and locally constant linear trend model. Then the recursive least squares with exponential factor is applied to shallow trench isolation etch process to predict the future etch rate. The etch process, which is a critical process in the flash memory manufacturing, is known to suffer from significant etch rate drift due to chamber seasoning. In order to handle the metrology delay, we propose a new time update scheme. RLS with the new time update method gives very good result. The estimate error variance is smaller than that from EWMA, and mean square error decrease more than 10% compared to that from EWMA.

Paper Details

Date Published: 1 July 2003
PDF: 12 pages
Proc. SPIE 5044, Advanced Process Control and Automation, (1 July 2003); doi: 10.1117/12.485293
Show Author Affiliations
Jin Wang, Advanced Micro Devices, Inc. (United States)
S. Joe Qin, Univ. of Texas at Austin (United States)
Christopher A. Bode, Advanced Micro Devices, Inc. (United States)
Matthew A. Purdy, Advanced Micro Devices, Inc. (United States)


Published in SPIE Proceedings Vol. 5044:
Advanced Process Control and Automation
Matt Hankinson; Christopher P. Ausschnitt, Editor(s)

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