Performance analysis by simulation of floating-gate MOSFETs applied on a bidirectional associative memory architecture

Abstract

Floating-gate MOSFETs (FGMOSFETs) are devices that can be electrically programmable and have a non-volatile characteristic. This feature can be adopted to con® gure a basic cell performing as a variable resistance that can be applied in arti® cial neural networks as a synapse. Based on a simple model and considering the coupling coe cient of the structure as the gain of a voltage controlled voltage source, the electrical characteristics of a¯oating-gate MOSFET can be simulated in PSpice and an arti® cial neural net, such as the bidirectional associative memory (BAM), can be implemented. Therefore a performance analysis of the net may be done with diOE erent sets of threshold voltages for the FGMOSFETs con® gured as a CMOS inverter used as a synapse. The objective is to know pattern pairs in a bidirectional way. The result is a correlation matrix for the BAM as a function of an electrical parameter of the devices, which is directly related to the respective matrix calculated by the matrix dot product, using the method outlined by Kosko.