Byung‐Gook Park

Sung Yun Woo, Dongseok Kwon, Byung-Gook Park et al.

Positive-feedback (PF) device and its operation scheme to implement pulse width modulation (PWM) function was proposed and demonstrated, and the device operation mechanism for implementing PWM function was analyzed. By adjusting the amount of the charge stored in the n- floating body (Qn), the potential of the floating body linearly changes with time. When Qn reaches to a threshold value (Qth), the PF device turns on abruptly. From the linear time-varying property of Qn and the gate bias dependency of Qth, fully functionable PWM neuron properties including voltage to pulse width conversion and hard-sigmoid activation function were successfully obtained from a single PF device. A PWM neuron can be implemented by using a single PF device, thus it is beneficial to extremely reduce the area of a PWM neuron circuit than the previously reported one.

Soochang Lee, Chul-Heung Kim, Seongbin Oh et al.

We present a two-layer fully connected neuromorphic system based on a thin-film transistor (TFT)-type NOR flash memory array with multiple postsynaptic (POST) neurons. Unsupervised online learning by spike-timing-dependent plasticity (STDP) on the binary MNIST handwritten datasets is implemented, and its recognition result is determined by measuring firing rate of POST neurons. Using a proposed learning scheme, we investigate the impact of the number of POST neurons in terms of recognition rate. In this neuromorphic system, lateral inhibition function and homeostatic property are exploited for competitive learning of multiple POST neurons. The simulation results demonstrate unsupervised online learning of the full black-and-white MNIST handwritten digits by STDP, which indicates the performance of pattern recognition and classification without preprocessing of input patterns.