Dr Yuanyuan Shi
PhD Graduate
Department of Engineering
University of Barcelona
Yuanyuan Shi received her bachelor's degree in 2013, and master's degree in 2015. She has just defended her PhD thesis about two dimensional materials based electronic synapses for neuromorphic applications in July 2018 at University of Barcelona in Spain. She was a visiting PhD student at Stanford University for one year. Until now, Yuanyuan has already published 38 research papers (13 as first author, including Nature Electronics, IEDM etc), one book chapter and registered two international patents, one of which (WO2016/074305) has received 1 M$ investment to create a start-up and introducing this product in the market. She has attended 12 international conferences, and given 6 talks in these conferences. She has received the national award from the Ministry of Education of China, and is a student member of Royal Society of Chemistry (RSC), IEEE and Electron Device Society (EDS). She is also severing as an active reviewer for several international journals, such as Scientific Reports, Thin Solid Films, ChemElectroChem and others.
Two Dimensional Materials Based Electronic Synapses for Neuromorphic Applications
Electronic machines and computers have experienced a huge development during the last four decades, mainly thanks to the continuous scaling down of the hardware responsible of information processing and storage (i.e. transistors). However, as the size of these devices approaches inter-atomic distances, the fabrication costs increase exponentially. In order to solve this problem, the industry has started to consider new system architectures and hardware for processing and storing information. Inspired by nature, scientists and engineers have focused their attention on the human brain, which is the most powerful system known.
The human brain can easily perform an infinity of operations that computers cannot do, it can naturally learn by adapting its physical structure, and it consumes much less energy. The reason is that human brains use a very sophisticated and dense neural network that process and stores the information in parallel. This massive parallelism is the genuine feature that even the most powerful computers developed to date cannot match, as they all rely in an architecture that process and stores information independently, creating a bottleneck that limits their performance. Therefore, emulating the functioning of the human brain using electronic circuits is extremely important, and it has become an obsession for the biggest enterprises.
The first artificial neural networks for artificial intelligence (AI) systems relied on the use of field effect transistors, as they has been the basis of all modern electronic devices. However, recent studies indicate that memristors may be more suitable to emulate the interaction between neurons. More specifically, two neurons interact to each other through a synapse, which is a thin membrane that change its resistivity based on the electrical impulses released by the two neurons. The structure and working principle of synapses is strikingly similar to that of memristors, which moreover show the advantage of a simpler structure and a lower fabrication cost compared to transistors.
However, not all memristors are suitable for emulating biological synapses. Most traditional memristors change their resistivity between two different states when a specific electrical impulse is applied. However, synapses change their resistivity with the time in a dynamic way, following some specific learning rules. In this PhD thesis I carry out a deep study about resistive switching in different materials, and I fabricate memristive devices that can accurately resemble several synaptic behaviors. One of the most innovative aspect of my investigation is that I use a new dielectric material (called hexagonal boron nitride) that holds a layered structure, and thanks to it my memristors show several properties never observed before.
