The advent of 4^th industrial revolution has led to breakthroughs in these high-tech products which are now capable of performing various intelligent tasks such as real-time big data analysis, self-driving automobile navigation, speech/face recognition. These tasks usually deal with a large amount of unstructured data using software-based artificial neural network (ANN). But, these software-based ANNs tend to be energy efficient when used in versatile cognitive systems, making it very challenging to apply it to battery-powered mobile electronics with limited battery capacity. The high power consumption of conventional computing hardware for performing software-based ANN is mainly due to the von Neumann architecture which is energy-efficient for data-intensive tasks. To overcome this issue, it is necessary to develop novel semiconductor device enabling energy-efficient computing architecture.

I developed brain-inspired nanoelectronic synaptic devices that can energy-efficiently process ANN like a human. I demonstrated that the poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane)(pV3D3)-based memristor and 3D fin-shaped field-effect transistor, also known as FinFET, with a poly-Si/SiO₂/Si₃N₄ gate stack on a single-crystalline silicon channel (SONS) devices can be operated as an electronic synapse device featuring analog conductance update. The pattern recognition for human face and MNIST handwritten digit is evaluated via device-to-system level simulation using the ANN based on pV3D3 memristor and SONS devices, showing the superior online learning accuracy of ~91%.

Furthermore, 3D NAND Flash memory, which is the main stream of the current flash memory market, can be powerful candidate for off-line neuromorphic system, because 3D NAND Flash memory has the highest bit density among existing semiconductor devices. In order to utilize 3D NAND Flash memory as a neuromorphic system, it is important to improve reliability, such as high-intensive read disturbance and retention characteristics. To improve the reliability of 3D NAND flash memory, I will conduct research on new materials and novel memory device structures. Due to the cell current shortage phenomenon that occurs as the number of stacks of 3D NAND flash memory increases and the device structure in which the Si₃N₄ trap layer between adjacent devices is connected, there are the interference and reliability issues between adjacent devices. Now, the global memory semiconductor companies are trying to solve the reliability problem of 3D NAND Flash memory through the introduction of new materials, but due to the insufficient new product development schedule, it is only developed only within the limited material library. However, the process itself has not been remarkably improved, thus we are improving the reliability with help from SSD controller. Therefore, I will conduct research to develop high-reliability memory devices with new materials such as 2D semiconductor materials and ferroelectric insulating films in order to be helpful to the industry. The results presented here will pave the way for development of energy-efficient, intelligent electronic system