Synaptic and neuron functionality in a single three terminal device leveraging the ferroionic control of the insulator-to-metal transition in a rare-earth-nickelates

Neuromorphic computing refers to the development of hardware that emulates the efficient way the brain processes information by exploiting the principles of parallelism, sparsity, and low power consumption. In the last decade, new device concepts and exotic materials have been explored to imitate the behaviour of neurons and synapses. However, demonstrations of the interaction between synaptic and neuron devices are very scarce, due to their different functional requirements and operating mechanisms. Moreover, to construct a fully functional neural network the individual artificial synapse and neuron devices are often integrated with transistors, capacitors, resistors and other peripheral circuits which greatly reduce the efficiency of the neuromorphic hardware. Therefore, a crucial step towards the practical integration of these systems in large-scale networks is to seamlessly integrate synaptic and neuronal functionalities using the same technology, preferably the same material and even (ideally) the same device. The unique electronic properties of strongly correlated oxides make them excellent candidates to face this challenge. The insulator-to-metal transitions in complex oxides can be tuned electronically in a non-volatile manner by several means, including ferroelectric effects, phase change transitions, valence variations, etc that can be used to demonstrate both neuron and synapse functionality. In this essence, this project SYANAPTRON aims at integrating neuron and synapse functionality in a single three-terminal nano-scale device, leveraging the co-existence and interplay of ferrionic (ferroelectricity and control of oxygen vacancies) and insulator-to-metal transitions in complex oxide heterostructures. The integration of synapse and neuron functionality in a single device is revolutionary as it will reduce the complex fabrication process, and peripheral circuitry, and enhance the packing density of future neuromorphic hardware systems.