This SBIR research project will develop and implement sensor-based models into an intelligent, adaptive, and neurally-inspired control system for a high-density, low-pressure plasma process on a microwave plasma reactor system. The goal is to address the need for nonlinear and adaptive control methods created by the uncertainty in sensors and components of the microfactory/clustor tools. Special interfaces tailored for the control system will render the time response of the sensors, the neuro-controller, and the actuators to within 1-5 seconds, which is fast enough to achieve real--time and in-situ control. Moreover, the use of analog VLSI chips in implementing the algorithms results in reducing the learning time to, at most, a few milliseconds. This savings in the computational requirement will ensure ample time for precise and fast actions.
