Closed loop adaptive control of spectrum-producing step using neural networks
Abstract
Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optionalmore »
- Inventors:
- Issue Date:
- Research Org.:
- Univ. of California (United States)
- Sponsoring Org.:
- USDOE, Washington, DC (United States)
- OSTI Identifier:
- 321223
- Patent Number(s):
- 5841651
- Application Number:
- PAN: 7-973,865
- Assignee:
- Dept. of Energy, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-48
- Resource Type:
- Patent
- Resource Relation:
- Other Information: PBD: 24 Nov 1998
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; PLASMA DIAGNOSTICS; SPECTROSCOPY; MANUFACTURING; NEURAL NETWORKS; ON-LINE MEASUREMENT SYSTEMS; DATA ANALYSIS; PROCESS CONTROL; CHEMICAL REACTORS; VISIBLE SPECTRA; CLOSED-LOOP CONTROL
Citation Formats
Fu, C Y. Closed loop adaptive control of spectrum-producing step using neural networks. United States: N. p., 1998.
Web.
Fu, C Y. Closed loop adaptive control of spectrum-producing step using neural networks. United States.
Fu, C Y. Tue .
"Closed loop adaptive control of spectrum-producing step using neural networks". United States.
@article{osti_321223,
title = {Closed loop adaptive control of spectrum-producing step using neural networks},
author = {Fu, C Y},
abstractNote = {Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Nov 24 00:00:00 EST 1998},
month = {Tue Nov 24 00:00:00 EST 1998}
}