Technology independent circuit sizing for fundamental analog circuits using artificial neural networks

Kahraman, Nihan; Yıldırım, Tülay

doi:10.1109/rme.2008.4595710

Cited by 21 publications

(10 citation statements)

References 6 publications

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“…A neuron is one of the basic elements in ANN model which comprises of set of inputs, weight coefficients, also known as synaptic weights, and an activation function [15]. Neurons form the basis of an input layer that has sensory units to collect the information from its environment, an output layer and a number of optional intermediate layer(s) called hidden layers.…”

Section: Construction Of Ann Modelsmentioning

confidence: 99%

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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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Section: Construction Of Ann Modelsmentioning

confidence: 99%

“…Further, in [14] a solution for OTA circuits modelling has been proposed. An illustration of technology independent circuits sizing for basic analog circuits has been described in [15].…”

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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“…A method with simulation-in-loop that reduces the iterations is presented in [30]. Recently, a novel ANN based approach for the circuit sizing that develops ANN models for basic building blocks like current mirrors and differential amplifiers is reported in [31]. Accuracy in analog domain is reported to be 90% and the concept is extended in digital domain with an accuracy of 64% [32].…”

Section: Optimization Based Designmentioning

confidence: 99%

“…Artificial Neural Network Binned Models [28,29] Artificial Neural Network Correction Models [30,31] Apply constraint conditions fast, yet accurate answers to the learnt task. To begin with, a neural network has no idea about the functional relationship that it has to emulate.…”

Section: Brief Introduction To Annmentioning

confidence: 99%

A new approach to sizing analog CMOS building blocks using pre-compiled neural network models

Mendhurwar

Sundani

Aggarwal

et al. 2011

Analog Integr Circ Sig Process

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There has been a constant endeavor towards improving the available circuit design automation tools to match technological advancements in the electronic industry. However, inadequate research efforts in the analog domain are holding back the exploitation of advanced technologies. A dearth of design expertise in the analog domain is the principal driving force for the growth of Design Automation (DA) tools. Transistor sizing is one of the most crucial steps in the analog IC design. In this paper, we put forward a new computer aided design framework for the sizing of transistors in MOS Integrated Circuit (IC) amplifiers by incorporating powerful modeling capabilities of Artificial Neural Networks (ANN). ANNs have proven to be efficient and accurate modeling tools in several applications. The proposed tool is capable of directly computing transistor related design parameters, of the MOS IC amplifier and associated peripheral circuitry. The proposed tool thus avoids several time-consuming simulations and/or tuning runs at the very bottom level of analog IC amplifier implementation, using a given CMOS process. It also reduces manual intervention in the design process, thus enhancing the automation of the design process. This paper presents design examples of several analog IC functional modules that are developed and verified successfully.

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“…Artificial neural network (ANN) has been used in [11] for deciding the MOSFET channel length and width for analog integrated circuits. A technique for technology independent neural network modeling for fundamental blocks of analog circuits has been discussed in [12]. ANN has been used in [13] for modeling and design of CMOS operational amplifier circuit.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Design of a Nano Scale CMOS Inverter for Symmetric Switching Characteristics

Mukhopadhyay

Pandit

2012

VLSI Design

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This paper presents a technique for the modeling and design of a nano scale CMOS inverter circuit using artificial neural network and particle swarm optimization algorithm such that the switching characteristics of the circuit is symmetric, that is, has nearly equal rise and fall time and equal output high-to-low and low-to-high propagation delay. The channel width of the transistors and the load capacitor value are taken as design parameters. The designed circuit has been implemented at the transistor-level and simulated using TSPICE for 45 nm process technology. The PSO-generated results have been compared with SPICE results. A very good accuracy has been achieved. In addition, the advantage of the present approach over an existing approach for the same purpose has been demonstrated through simulation results.

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Technology independent circuit sizing for fundamental analog circuits using artificial neural networks

Cited by 21 publications

References 6 publications

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

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

A new approach to sizing analog CMOS building blocks using pre-compiled neural network models

Modeling and Design of a Nano Scale CMOS Inverter for Symmetric Switching Characteristics

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