LAUSR

We report on a 1-transisor/2-resistor (1T2R) synapse device with improved conductance linearity and conductance ratio under an identical pulse condition for hardware neural networks with high pattern-recognition accuracy. Utilizing an additional series-connected resistor, the conductance linearity of a synapse device was significantly improved owing to the reduced initial voltage drop on an resistive RAM (RRAM) device during depression conditions. Moreover, to maximize the conductance ratio of a synapse device, we utilized a steep subthreshold region of an MOSFET by a parallel connection of an RRAM and a transistor. A small change in voltage on the RRAM directly controlled the gate bias of the MOSFET, which causes a large change in the drain current. Compared with a conventional RRAM synapse device, the 1T2R synapse device shows an improved conductance linearity and conductance ratio ( $> \times 100$ ). Finally, we confirmed an excellent classification accuracy by using a neural network simulation based on a multilayer perceptron.