Implementability restrictions of the beta-CMOS artificial neuron

Author

Varshavsky, Victor ; Marakhovsky, Vyacheslav

Author_Institution

Aizu Univ., Fukushima, Japan

Volume

1

fYear

1999

fDate

1999

Firstpage

401

Abstract

The paper is focused on the functional possibilities, parameter stability and learnability of the artificial learnable neuron implemented on the base of CMOS β-driven threshold element. A neuron β-comparator circuit is suggested with a very high sensitivity to input current change that allows us to sharply increase the threshold value of the functions. The SPICE simulation results confirm that the neuron is learnable to realize threshold functions of 10, 11 and 12 variables with maximum values of threshold 89, 144 and 233 respectively. A number of experiments were conducted to determine the limits in which the working parameters of the neuron can change providing its stable functioning after learning the functions for each of these threshold values. MOSIS BSIM3v3.1 0.8 μm transistor models were used in the SPICE simulation

Keywords

CMOS integrated circuits; SPICE; circuit simulation; circuit stability; learning (artificial intelligence); network parameters; neural chips; 0.8 micron; BSIM3v3.1; CMOS β-driven threshold element; MOSIS; SPICE simulation; artificial learnable neuron; beta-CMOS artificial neuron; input current change; learnability; parameter stability; threshold value; threshold values; transistor models; working parameters; Equivalent circuits; Joining processes; Neurons; Petroleum; SPICE; Sun; Voltage control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on

Conference_Location

Pafos

Print_ISBN

0-7803-5682-9

Type

conf

DOI

10.1109/ICECS.1999.812307

Filename

812307