In this paper, we reviewed the recent trends on neuromorphic computing using emerging memory technologies. Two representative learning algorithms used to implement a hardwarebased neural network are described as a bio-inspired learning algorithm and software-based learning algorithm, in particular back-propagation. The requirements of the synaptic device to apply each algorithm were analyzed. Then, we reviewed the research trends of synaptic devices to implement an artificial neural network.

This work investigates the intrinsic characteristics of multilayer WSe2 field effect transistors (FETs) by analysing Pulsed I-V (PIV) and DC characteristics measured at various temperatures. In DC measurement, unwanted charge trapping due to the gate bias stress results in I-V curves different from the intrinsic characteristic. However, PIV reduces the effect of gate bias stress so that intrinsic characteristic of WSe2 FETs is obtained. The parameters such as hysteresis, field effect mobility (μeff), subthreshold slope (SS), and threshold voltage (V th) measured by PIV are significantly different from those obtained by DC measurement. In PIV results, the hysteresis is considerably reduced compared with DC measurement, because the charge trapping effect is significantly reduced. With increasing temperature, the field effect mobility (μeff) and subthreshold swing (SS) are deteriorated, and threshold voltage (V th) decreases.