High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks.

For current microelectronic integrated systems, the design methodology involves different steps that end up in the full system simulation by means of electrical and physical models prior to its manufacture. However, the higher the circuit complexity, the more time is required to complete these simulations, jeopardizing the convergence of the numerical methods and, hence, meaning that the reliability of the results are not guaranteed. This paper shows the use of a high-level tool based on Matlab to simulate the operation of an artificial neural network implemented in a mixed analog-digital CMOS process, intended for sensor calibration purposes. The proposed standard tool enables modification of the neural model architecture to adapt its characteristics to those of the electronic system, resulting in accurate behavioral models that predict the complete microelectronic IC system behavior under different operation conditions before its physical implementation with a simple, time-efficient, and reliable solution.

Analysis and simulation of a mixed-mode neuron architecture for sensor conditioning.

The design, analysis, and system simulation of an adaptive processor based on a current-mode mixed analog-digital circuit is presented. The processor consists of a mixed four-quadrant multiplier and a current conveyor that performs the nonlinearity. Schematics, circuit parameters, and a high-level model are shown. The results achieved when applying this processor model to conditioning several sensor types are discussed.