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.
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.
This paper is focused on the linearization of the radio frequency power amplifier of a professional digital handheld by means of an artificial neural network. The simplicity of the neural network that is used, together with the fact that a feedback path is unnecessary, make this solution ideal to reduce both the cost of a handheld and its hardware complexity, while fully maintaining its performance. A compensation system is also needed to keep the linearization characteristics of the neural network stable against frequency, temperature and voltage variations. The whole solution that comprises both the neural network and the compensation system has been implemented in the DSP of a real handheld and afterwards fully tested. It has proved to be satisfactory to meet the telecommunication standard requirements in all frequency, temperature and voltage ranges under consideration, while efficient to lower the computational cost of the handheld and to make its internal hardware simpler in comparison with other traditional linearization techniques. The results obtained demonstrate that a neural network can be used to linearize the power amplifiers that are used in transmitters of telecommunication equipment, leading to a significant reduction of both their hardware cost and complexity.
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