A Study on the Linearity Synapse Transistor in Self Learning Neural Network

자기인지 신경회로망에서 선형 시냅스 트랜지스터에 관한 연구

A VLSI implementation of a self-learning neural network integrated circuits using a linearity synapse transistor is investigated. The thickness dependence of oxide current density, stress current, transient current and channel current has been measured in oxides with thicknesses between 41 and 112 $\AA$, which have the channel width$\times$length 10$\times$1${\mu}{\textrm}{m}$ respectively. The transient current will affect data retention in synapse transistors and the stress current is used to estimate to fundamental limitations on oxide thicknesses. The synapse transistor has represented the neural states and the manipulation which gave unipolar weights. The weight value of synapse transistor was caused by the bias conditions. Excitatory state and inhitory state according to weighted values affected the drain source current.