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 distanc...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/663415 |
| Acceso en línea: | http://hdl.handle.net/10803/663415 |
| Access Level: | acceso abierto |
| Palabra clave: | Nanoelectrònica Nanoelectrónica Nanoelectronics Nitrur de bor Nitruro de boro Boron nitride Enginyeria d'ordinadors Ingeniería de ordenadores Computer engineering Sinapsi Sinapsis Synapses Ciències Experimentals i Matemàtiques 53 |
| Sumario: | 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. For example, Au/Ti/h-BN/Cu devices exhibit the coexistence of bipolar and threshold RS, which can be controlled by using different current limitations. The devices do not require forming process, due to the present of native defects in the h-BN stack during the growth. Doping the Cu substrate with Ni results in a lower amount of native defects, which reduces the current in high resistive state (but these devices require the use of a forming process). For both Cu and Ni-doped Cu electrodes, the current ON/OFF ratio can be improved by increasing the thickness of the h-BN stack. In Au/Ti/graphene/h-BN/graphene/Au devices the switching voltages increase and the currents in high resistive state are smaller than in the devices without graphene. The most probable reason for this observation is that multilayer graphene can block and slow down the migration of ions between the h-BN and the electrodes. Metal/h-BN/metal electronic synapses show an unprecedented relaxation process with very low variability in hundreds of cycles, and the power consumption is very low in both standby and volatile regime (i.e. 0.1 fW and 600 pW, respectively). |
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