Publication

ANN Based Cryogenic DC Modeling of a 16 nm nMOS FinFET

2026

Abstract

An artificial neural network (ANN) based cryogenic DC I/V model for a 16-nm nMOS FinFET is presented and validated over the temperature range from 2.95 K to 300 K. The model is trained on measured drain-current data and compared against an analytical empirical model to evaluate accuracy, generalization capability, and computational efficiency. Several multilayer perceptron architectures are investigated, focusing on interpolation and extrapolation performance beyond the training bias space. The results demonstrate that the proposed ANN models accurately reproduce the drain current behavior and achieve excellent performance. When implemented in a circuit-level simulator, compact ANN architectures exhibit simulation times comparable to analytical models. Overall, the proposed approach represents a viable and efficient alternative for cryogenic FinFET modeling.

Official source

https://infoscience.epfl.ch/entities/publication/d5a0fb52-5005-4ba4-bcb7-63dc8af5cf5d

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2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/d5a0fb52-5005-4ba4-bcb7-63dc8af5cf5d

About this result

Related concepts (32)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Transistor count

The transistor count is the number of transistors in an electronic device (typically on a single substrate or "chip"). It is the most common measure of integrated circuit complexity (although the majority of transistors in modern microprocessors are contained in the cache memories, which consist mostly of the same memory cell circuits replicated many times). The rate at which MOS transistor counts have increased generally follows Moore's law, which observed that the transistor count doubles approximately every two years.

Multilayer perceptron

A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not linearly separable. It is a misnomer because the original perceptron used a Heaviside step function, instead of a nonlinear kind of activation function (used by modern networks).

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ANN Based Cryogenic DC Modeling of a 16 nm nMOS FinFET

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