The accounting of the simultaneous exposure of the low temperatures and the penetrating radiation at the circuit simulation of the BiJFET analog interfaces of the sensors
The article considers a choice of CAD system and SPICE-models for the circuit simulation of characteristics of the bipolar (BiJFET) analog integrated circuits (IC) at the exposure of the penetrating radiation (PR) and the low temperatures. The authors suggest a modified SPICE-model, which describes the nonmonotonic change of the peak drain current and the slope of the p-n junction FET (JFET) within the temperature range from −200°C to 30°C.
Automation of parameter extraction procedure for Si JFET SPICE model in the −200…+110°C temperature range
Through the analysis of JFET models with account for temperature effects, the most suitable one was selected as a basis for expanding SPICE modeling possibilities to cryogenic-to-high temperature range. Parameter extraction procedure was automated and improved using IC-CAP scripting capabilities.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.