Compact Modeling of Single Electron Memory Based on Perceptron Designs

In this work, we present a Single electron random access memories based on perceptron designs used as the basic artificial bio-inspired neural processing element. The operation principles are described and illustrated for the first time by simulations results. Combining the Monte Carlo method with a direct solution of the stationary master equation, we use SIMON simulator and MATLAB for training process. The main goal of this work is to build a multilayer neural network used in recognition and classification using single electron devices. We further provide a write/Erase/ Read states chronogram to provide the key element of our work which is the charge stored in output neuron’s quantum dots.

[1]  A. Kalboussi,et al.  Neural Circuitry Based on Single Electron Transistors and Single Electron Memories , 2014 .

[2]  C.A.L. Bailer-Jones,et al.  An introduction to artificial neural networks , 2001 .

[3]  A. Kalboussi,et al.  A SPICE model for single electron transistor applications at low temperatures: Inverter and ring oscillator , 2008, 2008 3rd International Conference on Design and Technology of Integrated Systems in Nanoscale Era.

[4]  Jacques-Olivier Klein,et al.  Nanodevice-based novel computing paradigms and the neuromorphic approach , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[5]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[6]  Konstantin K. Likharev,et al.  Coulomb blockade of single-electron tunneling, and coherent oscillations in small tunnel junctions , 1986 .

[7]  Akira Fujiwara,et al.  Silicon single-electron devices and their applications , 2004, Proceedings. 7th International Conference on Solid-State and Integrated Circuits Technology, 2004..

[8]  Dmitri B. Strukov,et al.  3D CMOS-memristor hybrid circuits: devices, integration, architecture, and applications , 2012, ISPD '12.

[9]  Adel Kalboussi,et al.  Simulation of Single Electron Transistor Inverter Neuron: Memory Application , 2013 .

[10]  J. Flak,et al.  Programmable CNN cell based on SET transistors , 2006, 2006 10th International Workshop on Cellular Neural Networks and Their Applications.

[11]  Jean,et al.  The Computer and the Brain , 1989, Annals of the History of Computing.

[12]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[13]  Konstantin K. Likharev,et al.  Single-electron devices and their applications , 1999, Proc. IEEE.

[14]  B. Irie,et al.  Capabilities of three-layered perceptrons , 1988, IEEE 1988 International Conference on Neural Networks.

[15]  P. Hadley,et al.  Simulating Hybrid Circuits of Single-Electron Transistors and Field-Effect Transistors , 2003 .

[16]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .

[17]  John R. Tucker,et al.  Complementary digital logic based on the ``Coulomb blockade'' , 1992 .

[18]  Jaap Hoekstra,et al.  On the impulse circuit model for the single‐electron tunnelling junction , 2004, Int. J. Circuit Theory Appl..

[19]  José Camargo da Costa,et al.  Design of a Hamming neural network based on single-electron tunneling devices , 2006, Microelectron. J..