A unified discussion on signal filtering in PID control

The major input signals entering the PID controller are; the set point, the process output, and measurable load disturbances. By feeding these signals through suitable filters, the properties of the feedback loop can be improved significantly. The goal of this paper is to provide a unified discussion about these filters. The paper treats set-point handling, feedforward from load disturbances, TITO (two input two output) control, noise filtering, and process dynamics compensation. An industrial case from the steel industry is also presented.     

Stability analysis and optimal tuning of EWMA controllers – Gain adaptation vs. intercept adaptation

Exponentially weighted moving average (EWMA) controllers are the most commonly used run-to-run controllers in semiconductor manufacturing industry. An EWMA controller can be implemented in two different ways. One way is to keep the process gain as its off-line estimate and update the intercept term at each run, which is termed EWMA with intercept adaptation; the other is to keep the intercept term as its off-line estimate and update the process gain at each run, which is termed EWMA with gain adaptation. Despite the fact that gain variation and adaptation is typical in semiconductor industry, most EWMA formulations are for intercept adaptation and few results exist on the stability and sensitivity of EWMA with gain adaptation. In this paper, we propose a general formulation to analyze the stability of both EWMA controllers. The proposed state-space representation not only reveals the similarities and differences between two types of EWMA controllers, but also explains why the stability conditions for both types of EWMA controllers are independent of process disturbances. In addition, we propose a general framework that unifies the analysis of the optimal control performance for both types of EWMA controllers. The proposed framework is different from existing approaches in that it decouples the state estimation from the control law, and derives the optimal weighting based on the state estimation performance. The proposed framework significantly simplifies the analysis procedure, especially for EWMA with gain adaptation. Using this framework, we derive the optimal EWMA weighting through solving the discrete-time algebraic Riccati equation (DARE) for various process disturbances that are encountered in semiconductor manufacturing industry. Simulation examples are given to illustrate the optimality of the EWMA weighting derived using the framework. Some practical aspects of controller tuning are also discussed based on the simulation results.     

A simple method for detection of stiction in control valves

A simple method diagnosing oscillations in process control loops is presented. The new method is based on the cross-correlation between control input and process output and it is shown to correctly distinguish between two important reasons for oscillations in control loops in the process industry, namely external oscillating disturbances and static friction (stiction) in control valves.     

Intelligent adaptive process control using dynamic deadband for semiconductor manufacturing

This paper proposes an efficient control method to minimize process error and to reduce process variance in semiconductor manufacturing. The photolithography (photo) process forms a complex semiconductor circuit and is important for quality. Obstacles to the process include the facility itself, vibration, wear and tear, product/process changes and environmental influences. Control methodologies being currently used to address these issues often amplify the variation of the process by failing to perform adequate process control. Therefore, this paper proposes an effective process control method to reduce process variance by quickly detecting and identifying process disturbances and accurately reflecting the degree of change to process control. This study proposes dynamic deadband control that uses a region (band) to detect the status of a process change. It adjusts the process control based on the changes detected. In this research, the semiconductor manufacturing company is supported to perform control that is more precise and reduces fluctuations by producing products of uniform quality. In addition, it can contribute to yield due to the quality incentive and increased process control of semiconductor manufacturing.     

基于模糊规则的液位控制系统

针对过程．工业中的液位控制问题，为弥补常规液位控制方法在应用中的不足，根据液位装置操作的特点，采用模糊控制方法，提出了一种基于模糊规则的液位控制系统。该方法针对影响液位的可测扰动采用前馈控制加以补偿，既避免了在线优化的过程，又增强了系统对随机扰动的鲁棒性，在实际工业应用中取得了理想的控制效果。     

Thermal transfer for flat‐panel‐display manufacturing

Abstract— Photolithography is currently the predominant patterning method in the flat‐panel‐display (FPD) industry. Thermal lithography is a novel approach offering superior process control, a completely dry process, and considerable cost savings. Thermal imaging is now the dominant imaging method in computer‐to‐plate applications in the printing industry, with over 6000 installations world‐wide. Two applications, in which this technology could be applied in the FPD industry, will be discussed in detail: color filters for liquid‐crystal displays (LCDs) and barrier ribs for LCDs and organic light‐emitting‐diode (OLED) displays.     

A disturbance observer enhanced composite cascade control with experimental studies

The presence of strong disturbances usually causes great performance degradation of industrial process control systems. A disturbance observer (DOB) enhanced composite cascade control consisting of model predictive control (MPC), proportional-integral-derivative (PID) control, and DOB is proposed in this paper. DOB is employed here to estimate the severe disturbances and the estimated values are applied for feed-forward compensation, forming a composite control together with MPC. To evaluate the efficiency and validity of the proposed control structure, the simulation as well as experimental studies have been carried out for a level tank process which represents a typical first-order plus dead-time (FODT) industry process. Both the simulation and experimental results show that the proposed composite control method significantly improves the disturbance attenuation property of the MPC scheme in controlling such a typical industrial process.     

Double EWMA controller using neural network-based tuning algorithm for MIMO non-squared systems

The double exponentially weighted moving average (dEWMA) control method is a popular algorithm for adjusting a process from run to run in semiconductor manufacturing. For MIMO non-squared statistic systems, the singular value decomposition (SVD) method is used for decoupling and the SVD-based dEWMA control scheme is treated as a MIMO extension of dEWMA control design. To enhance the performance and robustness of the linear system in the presence of ramp disturbances and white noises, the neural network-based adaptive algorithm is used to automatically tune the dEWMA controller parameters. Under the specified input patterns, the early stop criterion for the training-validation neural networks, and the stability constraints added in the tuning mechanism, the simulations show that the proposed control technique can effectively improve the means and standard deviations of the process outputs.     

A Kalman filter-based R2R control system with parallel stochastic disturbance models for semiconductor manufacturing processes

A Kalman filter-based run-to-run control system has been proposed for minimum variance control of semiconductor manufacturing process. In the proposed control system, both gain- and bias-varying process models combined with different stochastic disturbance models were considered and identified in parallel. The best-fit model is selected and used for the R2R controller design. Sub-models of the ARIMA(1,1,1) process were considered for stochastic modeling of the bias and gain variation, and the Kalman filters are used to find the optimum model parameter estimation. The control performance was analyzed for each case of the disturbance model to investigate the expected benefit from the control system in comparison with the EWMA filter-based controller.     

Recursive Back‐Stepping Design of An Adaptive Fuzzy Controller for Strict Output Feedback Nonlinear Systems

In this paper, a back‐stepping adaptive fuzzy controller is proposed for strict output feedback nonlinear systems. The unknown nonlinearity and external disturbances of such systems are considered. We assume that only the output of the system is available for measurement. As a result, two filters are constructed to estimate the states of strict output feedback systems. Since fuzzy systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory combined with a tuning function scheme is developed to derive the control laws of strict output feedback systems that possess unknown functions. Moreover, the H∞ performance condition is introduced to attenuate the effect of the modeling error and external disturbances. Finally, an example is simulated in order to confirm the applicability of the proposed method.