Perovskite neural trees

Abstract

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

Electrocatalytic Hydrolysis‐Modulated Multistate Resistive Switching Behaviors in Memristors

“…The resistant states maintain about 400 s without obvious degeneration, which is comparable with reported results. [ 48 ] Another point that needs to be clarified is the SET process of the device “water (w)” which happened in the negative voltage region. This phenomenon is due to dropping water on the top of the device.…”

Low-temperature emergent neuromorphic networks with correlated oxide devices

“…This behavior is described by simulation results shown in Fig. (44,47). However as soon as the currents flowing through the device are small, the latter effect can be neglected.…”

“…Homogeneously doping the material with hydrogen can yield a nonvolatile increase in electrical resistance ( 9 ). Recently, X-ray absorption and diffraction experiments have shown that the main mechanism of such colossal resistance change during hydrogenation lies in bringing in an extra electron to the nickel ions, while the changes in the crystal structure due to doping are very small ( 43 ). Since the electronic phase transition and concurrent resistance tuning is independent of temperature, it is possible to couple nickelate synapses with superconducting junctions to create neural networks.…”

“…However, it has been shown that the distribution of hydrogen can be changed by applying a voltage bias between different contacts, which can be the spiking voltage pulses created by the YBCO neurons discussed previously. The mechanism behind the redistribution of H dopants in films includes two main processes: drift of the charged ions in the electric field and thermodiffusion of ions in a temperature gradient created by Joule heating ( 43 , 46 ). However, as soon as the currents flowing through the device are small, the latter effect can be neglected.…”

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

“…In addition, its structure and performance are closely related to the composition of the system [17]. Perovskite-type oxides can form compounds through partial doping of metal ions at A and B sites on the basis of maintaining stable crystal structure, as well as controlling the A and B sites on the basis of maintaining stable crystal structure, as well as controlling the elements and valence states so that the performance of perovskite materials present rich diversity [18][19][20].…”