Electronic circuits modeling using artificial neural networks

“…A time domain artificial neural network (ANN) macro modeling concept is proposed. 18 The MOS transistor and nonlinear dynamic circuits are modeled by ANN. ANNs are also successfully applied in electronic circuits modeling several times, especially in the field of radio frequency circuits.…”

Section: Introductionmentioning

confidence: 99%

An enhanced operational amplifier macro model based on artificial neural network

Wei

Zhao

Wan

2022

Circuit Theory & Apps

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An enhanced operational amplifier (OPAMP) macro model based on artificial neural network (ANN) is developed for analog circuit simulation. The model uses ANN to capture the relation between the inputs and outputs (currents and voltages) of the circuit module. Both direct current (DC) and alternative current (AC) signals are considered in the model. By adopting the adaptive sampling algorithm, the amount of data required for model training can be significantly reduced and the accuracy of model fitting can be apparently improved. The model is also validated by the simulated data from OPAMP, Bandgap, and LDO circuits. Compared with SPICE model, enhanced ANN OPAMP macro model has nearly 8 times faster simulation speed without apparently degrading the model accuracy. The predictions made by the neural network are also compared to the experimental measurement results of LDO circuit fabricated in 0.18‐μm process. Both simulation and experimental results show the feasibility and accuracy of the proposed enhanced ANN OPAMP macro model.

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“…This renders them useful for solving a variety of problems in pattern recognition, prediction, optimization and associative memory [2]- [4]. Additionally, they are also being employed in circuit modelling [5].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Design of Inverter Threshold Quantization based Current Comparator using Artificial Neural Networks

Bhatia¹,

Pandey²,

Bhattacharyya³

2016

IJECE

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Performance of a MOS based circuit is highly influenced by the transistor dimensions chosen for that circuit. Thus, proper dimensioning of the transistors plays a key role in determining its overall performance. While choosing the dimension is critical, it is equally difficult, primarily due to complex mathematical formulations that come into play when moving into the submicron level. The drain current is the most affected parameter which in turn affects all other parameters. Thus, there is a constant quest to come up with techniques and procedure to simplify the dimensioning process while still keeping the parameters under check. This study presents one such novel technique to estimate the transistor dimensions for a current comparator structure, using the artificial neural networks approach. The approach uses Multilayer perceptrons as the artificial neural network architectures. The technique involves a two step process. In the first step, training and test data are obtained by doing SPICE simulations of modelled circuit using 0.18μm TSMC CMOS technology parameters. In the second step, this training and test data is applied to the developed neural network architecture using MATLAB R2007b.

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Electronic circuits modeling using artificial neural networks

Cited by 17 publications

References 14 publications

Application of Artificial Neural Networks in Electronics

Application of Artificial Neural Networks in Electronics

An enhanced operational amplifier macro model based on artificial neural network

Modelling and Design of Inverter Threshold Quantization based Current Comparator using Artificial Neural Networks

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