Intelligent control of low pressure plasma processing

Several parameters characterize systems for materials processing that use radio frequency electrical discharges in gases at low pressure. These include directly measurable quantities such as a DC bias voltage, an ion current, an energy flux, masses of charged species, and spectrally resolved optical emission. None of these is directly controllable but all are dependent on several variables that can be controlled such as radio-frequency (RF) power, chamber pressure, and gas flow rates. There is a rich parameter space that must be painstakingly searched for optimum conditions for any particular process. In place of the relatively slow manual procedure, an artificial intelligence (AI) approach has been used to map out contours for all of the above characteristic parameters in the control space. Automatic characterization of plasma systems in this way could significantly reduce the time to re-configure them and to transfer processes between different systems.