Abstract
This paper presents a basic block for building large-scale single-electron neural networks. This macro block is completely composed of SET inverter circuits. We present and discuss the basic parts of this device. The full design and simulation results were done using MATLAB and SIMON, which are a single-electron tunnel device and circuit simulator based on a Monte Carlo method. Special measures had to be taken in order to simulate this circuit correctly in SIMON and compare results with those of SPICE simulation done before. Moreover, we study part of the network as a memory cell with the idea of combining the extremely low-power properties of the SET and the compact design.
Highlights
Single-Electron Devices (SEDs) have attracted much attention since the 1980s when it was that they could be used to fabricate memory devices, low-power logic devices and high-performance sensors
The rationale behind neural networks and SingleElectron Transistors (SETs) devices is the possibility of taking advantages of both technologies [3] [4]: the high gain, speed of the SETs, learning capacity and problem-solving abilities of the Artificial Neural Networks (ANNs), in order to lead to Single-Electron Memories (SEMs) with low-power consumption, high density of the SETs and brain behavior qualities
We present simulations of the two main parts of an elementary neural network based on SET (Perceptron) using SIMON [5] simulator and MATLAB [6]
Summary
Single-Electron Devices (SEDs) have attracted much attention since the 1980s when it was that they could be used to fabricate memory devices, low-power logic devices and high-performance sensors. This device is made of an island connected through two tunneling junctions to a drain and a source electrode. The idea of combining Single-Electron Transistors (SETs) in neural networks architectures has raised considerable interest over recent years because of its potentially unique functionalities. The rationale behind neural networks and SET devices is the possibility of taking advantages of both technologies [3] [4]: the high gain, speed of the SETs, learning capacity and problem-solving abilities of the Artificial Neural Networks (ANNs), in order to lead to Single-Electron Memories (SEMs) with low-power consumption, high density of the SETs and brain behavior qualities. The idea was to present this neural network as a model of a “smart” SET/SEM device
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