Multi-terminal Transistor-like Devices Based on Strongly Correlated Metallic Oxides for Neuromorphic Applications

Tuning the superconducting performance of YBa2Cu3O7-δ films through field-induced oxygen doping

The exploration of metal-insulator transitions to produce field-induced reversible resistive switching effects has been a longstanding pursuit in materials science. Although the resistive switching effect in strongly correlated oxides is often associated with the creation or annihilation of oxygen vacancies, the underlying mechanisms behind this phenomenon are complex and, in many cases, still not clear. This study focuses on the analysis of the superconducting performance of cuprate YBa2Cu3O7-δ (YBCO) devices switched to different resistive states through gate voltage pulses. The goal is to evaluate the effect of field-induced oxygen diffusion on the magnetic field and angular dependence of the critical current density and identify the role of induced defects in the switching performance. Transition electron microscopy measurements indicate that field-induced transition to high resistance states occurs through the generation of YBa2Cu4O7 (Y124) intergrowths with a large amount of oxygen vacancies, in agreement with the obtained critical current density dependences. These results have significant implications for better understanding the mechanisms of field-induced oxygen doping in cuprate superconductors and their role on the superconducting performance.

The effects of point defect type, location, and density on the Schottky barrier height of Au/MoS2 heterojunction: a first-principles study

Using DFT calculations, we investigate the effects of the type, location, and density of point defects in monolayer MoS₂ on electronic structures and Schottky barrier heights (SBH) of Au/MoS₂ heterojunction. Three types of point defects in monolayer MoS₂, that is, S monovacancy, S divacancy and Mo_S (Mo substitution at S site) antisite defects, are considered. The following findings are revealed: (1) The SBH for the monolayer MoS₂ with these defects is universally higher than that for its defect-free counterpart. (2) S divacancy and Mo_S antisite defects increase the SBH to a larger extent than S monovacancy. (3) A defect located in the inner sublayer of MoS₂, which is adjacent to Au substrate, increases the SBH to a larger extent than that in the outer sublayer of MoS₂. (4) An increase in defect density increases the SBH. These findings indicate a large variation of SBH with the defect type, location, and concentration. We also compare our results with previously experimentally measured SBH for Au/MoS₂ contact and postulate possible reasons for the large differences among existing experimental measurements and between experimental measurements and theoretical predictions. The findings and insights revealed here may provide practical guidelines for modulation and optimization of SBH in Au/MoS₂ and similar heterojunctions via defect engineering.

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Artificial Intelligence has found many applications in the last decade due to increased computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses in the so-called Deep Neural Networks (DNNs). Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. As far as the training is concerned, we can distinguish between supervised and unsupervised learning. The former requires labelled data and is based on the iterative minimization of the output error using the stochastic gradient descent method followed by the recalculation of the strength of the synaptic connections (weights) with the backpropagation algorithm. On the other hand, unsupervised learning does not require data labeling and it is not based on explicit output error minimization. Conventional ANNs can function with supervised learning algorithms (perceptrons, multi-layer perceptrons, convolutional networks, etc.) but also with unsupervised learning rules (Kohonen networks, self-organizing maps, etc.). Besides, another type of neural networks are the so-called Spiking Neural Networks (SNNs) in which learning takes place through the superposition of voltage spikes launched by the neurons. Their behavior is much closer to the brain functioning mechanisms they can be used with supervised and unsupervised learning rules. Since learning and inference is based on short voltage spikes, energy efficiency improves substantially. Up to this moment, all these ANNs (spiking and conventional) have been implemented as software tools running on conventional computing units based on the von Neumann architecture. However, this approach reaches important limits due to the required computing power, physical size and energy consumption. This is particularly true for applications at the edge of the internet. Thus, there is an increasing interest in developing AI tools directly implemented in hardware for this type of applications. The first hardware demonstrations have been based on Complementary Metal-Oxide-Semiconductor (CMOS) circuits and specific communication protocols. However, to further increase training speed andenergy efficiency while reducing the system size, the combination of CMOS neuron circuits with memristor synapses is now being explored. It has also been pointed out that the short time non-volatility of some memristors may even allow fabricating purely memristive ANNs. The memristor is a new device (first demonstrated in solid-state in 2008) which behaves as a resistor with memory and which has been shown to have potentiation and depression properties similar to those of biological synapses. In this Special Issue, we explore the state of the art of neuromorphic circuits implementing neural networks with memristors for AI applications.