Neural network modelling of cold-cathode gauge parameters

Abstract

This article describes modelling of the operating characteristics of a cold-cathode ionisation gauge (CCG). 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 precise leak valve. Discharge intensity was measured vs. anode voltage at different pressures selected in the range from 1×10 −9 to 1×10 −5 mbar, and vs. pressure at different operating voltages ranging from 1.2 to 9 kV. In all cases the magnetic flux density was the same and amounted to about 0.13 T. The CCG exhibits an extremely low thermal outgassing rate and a low measurement limit. Therefore, it is suitable for pressure measurements in the ultrahigh vacuum range; however, it has a significant disadvantage. The discharge current vs. the pressure characteristic is non-linear and, in some cases, even discontinuous. The measured CCG characteristics were used as an input for the artificial neural network, which was used to generate a non-linear CCG input–output function used for linearisation purposes. It is generally known and strictly proven that neural networks are capable of learning and building any kind of real and non-polynomial input–output function. Furthermore, it was also mathematically proven that the single hidden neural layer system can learn any function. Other authors have reported that the learned function characteristics are not always continuous. In our experimental work, no mapping discontinuities in the formed model were detected. Despite the fact that learning of the input–output characteristics can be obtained by the neural networks with only one hidden layer, we have used the multilayer neural networks that exhibit a faster convergent and smoother learning process. The neural networks were trained to perform the transfer function between the input gauge parameters and the pressure. The neural networks are a suitable solution for CCG characteristics modelling and thus offer the possibility to overcome the disadvantages of the CCG.