Statistically based feedback control of photoresist application

Leang, S.; Spanos, Costas J.

doi:10.1109/asmc.1991.167407

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…In order to keep the development simple and to bring forth the transparency of the underlying reasoning, we will restrict our attention to the simplest process model, namely (1) 0894-6507/00$10.00 © 2000 IEEE where measurement at the end of run ; recipe (or input) employed for run ; system gain; disturbance that we would like to reject; other process noise over which we have no control. , in general, represents both the metrology induced noise, as well as, the normal lot-to-lot variability.…”

Section: Description Of the Problemmentioning

confidence: 99%

“…The resulting (three) models and the confidence one has in them drives the choice of both the controller structure and the numerical values of the coefficients. A growing body of literature exists on various approaches to R2R control, wherein different assumptions are placed on the three models [1]- [7] ( [8] provides a more complete list).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive optimization of run-to-run controllers: the EWMA example

Patel

Jenkins

2000

IEEE Trans. Semicond. Manufact.

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This paper presents a recursive scheme for optimizing the gain of an exponentiallly weighted moving average (EWMA) controller under stability constraints. The objective is to minimize the asymptotic mean square error in the output with minimal a priori information. The algorithm hinges on a simple representation of the optimal EWMA gain. Both step and drift disturbances are considered. It is shown that the gain sequence generated by the algorithm always yields a stable system. Furthermore, this sequence is shown to converge to a suboptimal value. Extensions to the algorithm to the case where there is model uncertainty are also presented. The algorithm is verified via simulation. Data from a manufacturing implementation is presented.Index Terms-Adaptive optimization, exponentially weighted moving average (EWMA) controller, run-to-run control (R2R), stochastic approximation.

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Section: Description Of the Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive optimization of run-to-run controllers: the EWMA example

Patel

Jenkins

2000

IEEE Trans. Semicond. Manufact.

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“…Once the T 2 statistic is calculated, it is plotted on a single-sided control chart whose upper control limit (UCL) can be formally set at the desired probability of erroneously stopping a good process, by using the F distribution [8]. (11) where N is the sample size during the production runs. Note that the sample size n used to calculate S is different from the sample size N used to determine the UCL.…”

Section: Malfunction Alarmsmentioning

confidence: 99%

“…Although the statistical techniques used in our controller are not novel [8], the manner they are used for process control is new, and results in improved capability of the entire process sequence. The control algorithms presented in this paper also have the additional advantage that they offer multivariate control and complex adaptation of non-linear models, without introducing extraneous variance in the process [9] [10] [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

A control system for photolithographic sequences

Leang

Thomson

et al. 1996

IEEE Trans. Semicond. Manufact.

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The goal of our control system is to improve the reliability, accuracy, and economy of operation of a sequence of interrelated processes. We achieve this task by using well known, rigorous statistical techniques to continuously monitor process parameters, detect out-of-control equipment, and then optimally adjust relevant machine inputs to bring the process back on target. We have implemented the supervisory control system on the photolithography sequence in the Berkeley Microfabrication Laboratory, where it has been conclusively proven that the supervisory control system increases significantly the capability of the entire process. The supervisory control algorithms, consisting of feedback and feed-forward control, multivariate, model-based statistical process control (SPC), and automated specification management algorithms, are independent of machine and/or process, and can be applied to any semiconductor manufacturing sequence.

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Designing response surface model-based run-by-run controllers: a worst case approach

Baras

Patel

1996

IEEE Trans. Comp., Packag., Manufact. Technol. C

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Abstract-This paper presents a framework for carrying out run-by-run (RbR) control via a deterministic worst-case approach. In particular the RbR controller developed tries to minimize the worst-case performance of the plant. This yields a methodology for handling uncertainty. A consequence of using the deterministic approach is that we no longer need any assumptions on the statistics of the noise. Rather, what we require is that the noise be bounded. Thus, we can also deal with nonGaussian and correlated noise. We provide results comparing the performance of the controller to a recursive least squares ( U S ) based controller.Index Terms-Run-by-run control, deterministic approach, bounded noise, worst-case design, ellipsoidal algorithms, response surface models.

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Statistically based feedback control of photoresist application

Cited by 14 publications

References 10 publications

Adaptive optimization of run-to-run controllers: the EWMA example

Adaptive optimization of run-to-run controllers: the EWMA example

A control system for photolithographic sequences

Designing response surface model-based run-by-run controllers: a worst case approach

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