NAND flash memory which is mature technology has great advantage in high density and great storage capacity per chip because cells are connected in series between a bit-line and a source-line. Therefore, NAND flash cell can be used as a synaptic device which is very useful for a high-density
synaptic array. In this paper, the effect of the word-line bias on the linearity of multi-level conductance steps of the NAND flash cell is investigated. A 3-layer perceptron network (784×200×10) is trained by a suitable weight update method for NAND flash memory using MNIST data
set. The linearity of multi-level conductance steps is improved as the word line bias increases from Vth −0.5 to Vth +1 at a fixed bit-line bias of 0.2 V. As a result, the learning accuracy is improved as the word-line bias increases from Vth
−0.5 to Vth+1.
Operation Scheme of Multi-Layer Neural Networks Using NAND Flash Memory as High-Density Synaptic Devices