Synaptic devices, which are considered as one of the most important components of neuromorphic system, require a memory effect to store weight values, a high integrity for compact system, and a wide window to guarantee an accurate programming between each weight level. In this regard,
memristive devices such as resistive random access memory (RRAM) and phase change memory (PCM) have been intensely studied; however, these devices have quite high current-level despite their state, which would be an issue if a deep and massive neural network is implemented with these devices
since a large amount of current-sum needs to flow through a single electrode line. Organic transistor is one of the potential candidates as synaptic device owing to flexibility and a low current drivability for low power consumption during inference. In this paper, we investigate the performance
and power consumption of neuromorphic system composed of organic synaptic transistors conducting a pattern recognition simulation with MNIST handwritten digit data set. It is analyzed according to threshold voltage (VT) window, device variation, and the number of available
states. The classification accuracy is not affected by VT window if the device variation is not considered, but the current sum ratio between answer node and the rest 9 nodes varies. In contrast, the accuracy is significantly degraded as increasing the device variation; however,
the classification rate is less affected when the number of device states is fewer.
