Modeling and control of microelectronics materials processing

Badgwell, Thomas A.; Breedijk, T.; Bushman, Scott; Butler, Stephanie Watts; Chatterjee, Sulagna; Edgar, Thomas F.; Toprac, Anthony J.; Trachtenberg, Isaac

doi:10.1016/0098-1354(94)e0038-o

Cited by 48 publications

(17 citation statements)

References 142 publications

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“…For example, an integrated circuit consists of several layers of chemically altered and patterned thin films in order to achieve desired electrical characteristics [13], [8]. The performance of such devices depends not only on the sharpness of these patterns and the interfaces between the layers of these films, but also heavily on the resulting microstructure of the film material.…”

Section: Introductionmentioning

confidence: 99%

Feedback control of surface roughness during thin-film growth using approximate low-order ODE model

Varshney

Armaou

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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The problem of feedback control of microstructure during thin film growth is addressed. The issue of the non-availability of closed form dynamic models for the evolution of microstructure is addressed by deriving a low-order statespace model that approximates the underlying kinetic monte carlo model. Initially a finite set of "coarse" observables is identified from spatial correlation functions to represent the coarse microscopic state that captures the dominant traits of the microstructure during the deposition process. Subsequently, a state-space model is identified, employing proper orthogonal decomposition and Carleman linearization, that describes the evolution of the coarse observables. The state space model is subsequently employed to design a receding horizon controller that regulates the surface roughness of the thin-film at a specified set point during the growth process by manipulating the substrate temperature. Closed-loop simulations at two distinct growth rates and in the presence of a step disturbance are performed to demonstrate the effectiveness of the controller.

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Section: Introductionmentioning

confidence: 99%

Feedback control of surface roughness during thin-film growth using approximate low-order ODE model

Varshney

Armaou

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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“…The modelling of plasma processes requires a thorough understanding of the plasma dynamics. The process is known to be sensitive to various parameters, such as chamber geometry, accuracy of measurement sensors, chemical disturbances, etc., [1].…”

Section: Introductionmentioning

confidence: 99%

On the closed-loop control of an argon plasma process

Iordanov¹,

Keville²,

Ringwood³

et al. 2006

IET Irish Signals and Systems Conference (ISSC 2006)

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Closed-loop control of a plasma process for etching applications is discussed in this study. Plasma processes are highly nonlinear systems that typically feature complex chemical and physical reactions. A laboratory-based plasma reactor is presented in this work, and issues concerning its closed-loop control are discussed. A PID controller for the experimental plasma process is developed and its performance is analysed. The need for a more advance control methodology is studied and some appropriate control structures are proposed. Finally, a description of the practical implementation of real-time multivariable closed-loop control for the studied plasma reactor is presented.

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“…More recently, however, there has been intense interest in controlling problems whose models have significantly higher complexity, necessitating discretizations of much higher order. Such problems include semiconductor manufacturing reactors (e.g., Badgewell et al, 1994;Aling et al, 1997), reverse-flow and circulating-loop reactors (e.g., Nieken et al, 1994;Hua et al, 1998), control of transitional hydrodynamic flows (Joshi et al, 1997;Carlson and Lumley, 1997), or instabilities in turbines (Badmus et al, 1996;Paduano et al, 1994). Problems with complicated geometries and/or sharp variations of solutions or system parameters make the use of larger discretization models unavoidable (Aling et al, 1996;Aling et al, 1997;Bangia et al, 1997;Graham et al, 1993;Chakravarti et al, 1995).…”

Section: Of Study Introductionmentioning

confidence: 99%