Two dimensional materials based electronic synapses for neuromorphic applications

[eng] 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).[spa] El cerebro humano puede realizar de forma sencilla infinidad de operaciones que los ordenadores no pueden hacer, pueden aprender naturalmente adaptando su estructura física, y consumen mucho menos energía. La razón es que el cerebro humano usa una sofisticada y muy densa red neuronal que procesa y almacena la información en paralelo. Este masivo paralelismo es la genuina característica que los ordenadores no pueden igualar, ya que éstos procesan y almacenan la información en unidades distintas, creando un embudo que limita sus prestaciones. Por lo tanto, emular el funcionamiento del cerebro utilizando componentes electrónicos es extremadamente importante, y se ha convertido en la obsesión de las mayores empresas. Las primeras redes neuronales artificiales para el desarrollo de inteligencia artificial están basadas en transistores, ya que éstos han sido la base de todos los dispositivos electrónicos modernos. Sin embargo, estudios recientes indican que los memristores podrían ser más idóneos para emular la interacción entre neuronas. En concreto, dos neuronas interactúan entre ellas a través de sinapsis, es decir, finas membranas que cambian su resistividad dependiendo de los impulsos eléctricos emitidos por las dos neuronas. La estructura y principio de funcionamiento de una sinapsis es muy similar al de un memristor, el cual presenta la ventaja de tener una estructura más simple y un coste de fabricación más bajo que un transistor. En esta tesis doctoral hemos desarrollado memristores avanzados utilizando materiales bidimensionales, como el grafeno y, especialmente, el nitruto de boto hexagonal con estructura multicapa. Nuestros experimentos y simulaciones indican que los dispositivos metal/h-BN/metal pueden ser utilizados como sinapsis electrónicas, ya que muestran comportamientos sinápticos en un único dispositivo. En nuestros dispositivos hemos observado short term plasticity, long term plasticity, spike timing dependent plasticity, y synapse relaxation. El régimen de funcionamiento puede ser controlado modificando la amplitud, duración e intervalo entre los pulsos aplicados. Además, las sinapsis electrónicas hechas mediante estructuras metal/h-BN/metal muestran un proceso de relajación muy repetitivo y con una baja variabilidad nunca observada anteriormente. Además, el consumo de potencia es muy bajo tanto en reposo (0.1 fW) como en modo volátil (600 pW)