Integrated identification and control for diffusion/CVD furnaces

Tsakalis, K.; Stoddard, K.

doi:10.1109/etfa.1997.616324

Cited by 19 publications

(12 citation statements)

References 4 publications

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“…Using the FLS approach, the tuning of the PID parameters was essentially a "one-pass" procedure. Furthermore, its performance was comparable to a multivariable H ∞ -based controller [50], in terms of disturbance attenuation and only somewhat worse in terms of zone matching and end-of-ramp overshoot. It should be mentioned, however, that the successful application of the decoupled PID structure relies on the low coupling between the heating zones.…”

Section: Integrated Identification and Pid Tuning For The Furnace Temmentioning

confidence: 82%

“…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%

“…Here, the uncertainty estimation is based on coprime factorizations and, more specifically, follows the methodology presented in [50]. The model structure is in the form P = D −1 N , where D and N are coprime factors.…”

Section: Uncertainty Description and Bound Estimation: Methodsmentioning

confidence: 99%

“…Based on practical experience, the main problems for such a design arise from the near-integrating nature of the spike subsystem and the differences in the dominant time-constants of each heating zone. While a full multivariable controller can address such problems in a unified manner [49,50], the PID solution is still of interest as a basic, safe controller mode. Moreover, PID controllers can be effective in practice for furnaces where the temperatures exhibit a low degree of coupling between zones, providing performance comparable to the full order multivariable solution.…”

Section: Integration Of System Identification With Frequency Loop-shamentioning

confidence: 99%

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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: Integrated Identification and Pid Tuning For The Furnace Temmentioning

confidence: 82%

Section: Uncertainty Description and Bound Estimation: Methodsmentioning

confidence: 99%

Section: Uncertainty Description and Bound Estimation: Methodsmentioning

confidence: 99%

Section: Integration Of System Identification With Frequency Loop-shamentioning

confidence: 99%

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Identification for PID Control

Tsakalis

Dash

2012

PID Control in the Third Millennium

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“…Using the method described in Ref. 15, a state space model is found, 16 that describes the dynamic behavior of the system from the power supply input to the external sensor measurement. ͑A state space model is a set of forced linear ordinary differential equations.͒ The DRS temperature, the temperature predicted by the model, and the identification signal are shown in Fig.…”

Section: Outer Loop Tuningmentioning

confidence: 99%

Closed-loop control of composition and temperature during the growth of InGaAs lattice matched to InP

Johnson

Grassi

Beaudoin

et al. 1999

Journal of Vacuum Science &Amp; Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena

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Articles you may be interested in͑Received 5 October 1998; accepted 27 November 1998͒ Spectroscopic ellipsometry ͑SE͒ and diffuse reflection spectroscopy ͑DRS͒ are used to control the Ga mole fraction and substrate temperature, respectively, during the growth of InGaAs lattice matched to InP. Ga mole fraction is controlled to within Ϯ0.002 of its target value and substrate temperature is controlled to within Ϯ2°C of its target value. The same growth under constant thermocouple control would result in a 50°C rise in real substrate temperature and a Ga composition 1% above its target value. In both cases, feedback control is achieved using a nested proportional-integral-derivative ͑PID͒ control loop, where, the inner loop consists of the conventional Eurotherm-thermocouple feedback loop and the outer loop updates the thermocouple setpoint using a PID control loop implemented in the control software. For composition control, the Ga cell thermocouple setpoint is increased ͑decreased͒ 0.2°C for every 0.002 that the Ga mole fraction, given by the SE sensor, deviates below ͑above͒ the target value. During substrate temperature control, the thermocouple setpoint is updated based on the temperature difference between the DRS sensor and the user setpoint. Frequency loop shaping, based on a dynamical model of the system obtained from an identification experiment, is used to tune the outer PID loop.

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Monitoring and control of semiconductor manufacturing processes

Limanond

Tsakalis

1998

IEEE Control Syst.

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Integrated identification and control for diffusion/CVD furnaces

Cited by 19 publications

References 4 publications

Identification for PID Control

Identification for PID Control

Closed-loop control of composition and temperature during the growth of InGaAs lattice matched to InP

Monitoring and control of semiconductor manufacturing processes

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