Cold-cathode ionization gauge - preprocessing and modelling of the gauge parameters using neural networks

• Published in: Materials and Technology Vol. 36; no. 6; pp. 401 - 405
• Main Authors: Belic, L I, Belic, I, Erjavec, B, Setina, J
• Format: Journal Article
• Language: English
• Published: 01.11.2002
• ISSN: 1580-2949

This article describes the modelling of the operating characteristics of a cold-cathode ionization gauge (CCG) using neural networks. The gauge characteristics were measured on a gauge-comparison UHV calibration system with a test chamber, an extractor gauge, a spinning rotor gauge, and a gas manifold with a variable leak valve. The discharge intensity was measured vs. the anode voltage at different pressures, selected in the range from 1DT10-9 mbar to 1DT105 mbar, and vs. pressure at different operating voltages ranging from 1.2 kV to 9 kV. In all cases the magnetic field density was the same and amounted to about 0.13 T. The CCG discharge current versus pressure characteristic is non-linear and in some cases even discontinuous. In our previous studies we found that neural networks are a very suitable tool for modelling the CCG input-output characteristics. Since CCGs are considered to be coarse vacuum gauges, modelling results with the maximum relative error within a 25% limit are quite acceptable. Our further research of modelling introduces the pre-processing of the measured data, where the originally measured data set is replaced with a filtered data set. The filtered CCG characteristics were used as an input for the artificial neural network, which was used to generate the non-linear CCG input -output function used for the linearization purposes. The neural networks were trained to perform the transfer function between the filtered input gauge parameters and the pressure. The modelling results were tested a separate, independent set of measured points.