Selective etching of silicon dioxide over silicon is a frequently used process in the manufacture of semiconductor devices. Limited diagnostic capabilities have forced plasma etch systems to rely on traditional statistical process control and recipes. With the addition of in situ measurements, however, automatic feedback control could be employed for control of the process variables. This paper focuses on the implementation and installation of a radio frequency power sensor suitable for advanced process control of plasma etching. The sensor measured information about the direct current bias voltage, radio fequency voltage, current, and phase angle at three locations in the power delivery system: before the matching network, after the matching network, and at the lower electrode. Matching network efficiency and transmission line analysis were used to transform between each measurement. This information showed the importance of accurate characterization of stray capacitance and inductance in the power delivery system. Plasma parameters of impedance, delivered power, sheath thickness, and sheath capacitance were computed using simple equivalent circuit models for the plasma discharge. Measurement of the fundamental and harmonic components of the voltage, current, and phase showed that the power generated in the plasma at the harmonic frequencies was approximately 3% of the generator power.Amplitudes of harmonic voltage matched analytical predictions.
In microelectronics manufacturing, control strategies for plasma etch systems have been limited to traditional statistical process control and recipe control techniques. The lack of in situ real-time measurements of process performance and appropriate models has hindered the introduction of feedback control systems. This paper focuses on empirical model building for advanced process control using two real-time diagnostic sensors for measurement of the reactor state. Laser interferometry for measurement of etch rate and voltage and current probes for measurement of effective radio-frequency power and sheath voltage, coupled with data acquisition hardware and software, provided the foundation for steadystate and dynamic model development of the plasma etch process. Several linear and nonlinear steady-state techniques including ordinary least squares, neural networks, and projection to latent structures were used in empirical model building. Both linear regression and recurrent neural network model structures provided a satisfactory fit of the data for the operating space investigated. Projection to latent structures techniques indicated that the most relevant variables were power, pressure, and chamber impedance. The addition of the impedance measurement significantly improved the predictive capability of the model. ) unless CC License in place (see abstract). ecsdl.org/site/terms_use address. Redistribution subject to ECS terms of use (see 128.83.63.180 Downloaded on 2015-06-05 to IP ) unless CC License in place (see abstract). ecsdl.org/site/terms_use address. Redistribution subject to ECS terms of use (see 128.83.63.180 Downloaded on 2015-06-05 to IP
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