Temperature control of diffusion/CVD furnaces using robust multivariable loop-shaping techniques

Tsakalis, Konstantinos; Flores-Godoy, J.J.; Stoddard, K.

doi:10.1109/cdc.1999.828019

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Our personal preference also follows similar guidelines with different emphasis in some details, such as, initial condition estimation and parameter estimate regularization. This method, described in more detail in [50,51], uses a multiple-input, single-output (MISO) approach and relies on a least squares parameter estimation algorithm to obtain parameter estimates for a linear model that describes the process locally around an operating point. For a multiple-input, single-output system and under an observability assumption the model is written as:ẋ…”

Section: Uncertainty Description and Bound Estimation: Methodsmentioning

confidence: 99%

Identification for PID Control

Tsakalis

Dash

2012

PID Control in the Third Millennium

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Proportional-Integral-Derivative (PID) controllers are still the most common control algorithms used in the process industry. PID is a reduced complexity controller that provides the bare essentials for control: an integrator for low-frequency disturbance attenuation, and two-zeros-worth of phase lead for stabilization and phase margin adjustment. Practical experience shows that its tuning can be accomplished with very little information about the plant from the point of view of standard design techniques. This celebrated feature carefully underplays the fact that as a limited degree of freedom controller, the PID may be unsuccessful in controlling arbitrary plants and the tuning techniques may become progressively more complicated as the class of plants is expanded. For example, it is quite straightforward to show that PID controllers can always stabilize a single integrator and that they cannot stabilize a chain of three integrators. Furthermore, techniques that have a well established track record for the typical process control application, e.g., a heating process, are shown to fail when the plant contains flexible modes, e.g., a pendulum with flexible shaft. It is in these cases where PID control must ultimately escape the back-of-the-envelope calculations and the nearly-model-free framework. The most popular controller can now utilize the most recent computational methods and design understanding. The benefits are in the hardware simplicity, including anti-windups and fast execution times, and ease of scheduling.Many methods for PID tuning are found in the literature, and [1] is an excellent introduction. Typical examples include an analytic derivation of the tuning, based on

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Section: Uncertainty Description and Bound Estimation: Methodsmentioning

confidence: 99%

Identification for PID Control

Tsakalis

Dash

2012

PID Control in the Third Millennium

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“…A wide range of control methods have been applied to temperature control of vertical furnaces, including frequency-domain and time-domain designs. Frequency domain based feedback controller design includes Proportional-Integral-Differential (PID) control [15]- [19], frequency domain loop shaping [20], [21], decentralized control [22], and H ∞ control [24]. These methods have the advantage that the design of controller gain parameters is intuitive because the stability margin and control bandwidth are illustrated by Bode and Nyquist diagrams.…”

Section: Introductionmentioning

confidence: 99%

Fast and Precise Temperature Control for a Semiconductor Vertical Furnace via Heater-Cooler Integration

Ohnishi

Hirata

Shibatsuji

et al. 2023

IEEE Trans. Semicond. Manufact.

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Semiconductor vertical furnaces must achieve even faster and more precise temperature control due to the demand for ever-reducing the minimum feature size or critical dimension in semiconductor chips. Also, the insulation performance of vertical furnaces has improved to reduce power consumption; as a side effect, temperature reduction operation takes longer without active cooling. Therefore, in addition to heaters, vertical furnaces equipped with coolers have emerged to achieve higher productivity, more precise temperature control, and lower power consumption. However, the inability to generate positive and negative control inputs for one of the heaters or coolers poses a challenge for an intuitive controller parameter design. The aim of this paper is to address these issues by proposing an intuitive method for designing a controller in the frequency domain which virtually integrates heaters and coolers. We implemented the controller in a full-scale actual semiconductor vertical furnace and first confirmed that the linearity is high. Furthermore, we experimentally verified that the controller achieves both high-speed, high-precision temperature control and low power consumption.

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Implementation of controller performance monitoring on a MIMO example

Tsakalis

Dash

2006

2006 American Control Conference

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Temperature control of diffusion/CVD furnaces using robust multivariable loop-shaping techniques

Cited by 7 publications

References 11 publications

Identification for PID Control

Identification for PID Control

Fast and Precise Temperature Control for a Semiconductor Vertical Furnace via Heater-Cooler Integration

Implementation of controller performance monitoring on a MIMO example

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