Manufacture of ultra large-scale integrated circuits involves accurate control of a challenging nonlinear Rapid Thermal Processing (RTP) system. Precise control of temperature profile and rapid ramp-up and ramp-down rates demanded by a RTP system cannot be achieved with conventional control strategies due to nonlinear and multi time-scale effects. In this paper the control of a RTP system is reformulated as an optimal multi-step sequential decision problem using the framework of finite horizon Markov decision processes and solved using a Reinforcement Learning (RL) algorithm. Three increasingly complex RL based control strategies are explored and compared with the existing state-of-the-art approach for controlling RTPs. Simulation results indicate that the approach proposed in this paper achieves superior control of the temperature profile and ramp-up and ramp-down rates for the RTP system. © 2018 Elsevier Ltd

Mathew M- Noel

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

Pradeep D.J

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Journal of Process Control

Most industrial processes exhibit inherent nonlinear characteristics. Hence, classical control strategies which use linearized models are not effective in achieving optimal control. In this paper an Artificial Neural Network (ANN) based reinforcement learning (RL) strategy is proposed for controlling a nonlinear interacting liquid level system. This ANN-RL control strategy takes advantage of the generalization, noise immunity and function approximation capabilities of the ANN and optimal decision making capabilities of the RL approach. Two different ANN-RL approaches for solving a generic nonlinear control problem are proposed and their performances are evaluated by applying them to two benchmark nonlinear liquid level control problems. Comparison of the ANN-RL approach is also made to a discretized state space based pure RL control strategy. Performance comparison on the benchmark nonlinear liquid level control problems indicate that the ANN-RL approach results in better control as evidenced by less oscillations, disturbance rejection and overshoot. © 2014 Elsevier B.V.

Applied Soft Computing

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Computers & Chemical Engineering

Modeling and temperature control of rapid thermal processing

IEEE Transactions on Semiconductor Manufacturing

Semi-Empirical Model-Based Multivariable Iterative Learning Control of an RTP System

Thin Solid Films

Model-based control in rapid thermal processing

Neural Computation

TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play

A Finite Horizon Markov Decision Process Based Reinforcement Learning Control of a Rapid Thermal Processing system

Journal	Data powered by TypesetJournal of Process Control
Publisher	Data powered by TypesetElsevier BV
ISSN	0959-1524
Open Access	0