Y₂O₃-based memristive crossbar array for synaptic learning

Abstract

Here, we report a fabrication of Y2O3-based memristive crossbar array along with an analytical model to evaluate the performance of such memristive array system to understand the forgetting and retention behavior in the neuromorphic computation. The developed analytical model is able to simulate the highly-dense memristive crossbar array based neural network of biological synapses. These biological synapses control the communication efficiency between neurons and can implement the learning capability of the neurons. During electrical stimulation of the memristive devices, the memory transition is exhibited along with the number of applied voltage pulses which is analogous to the real human brain functionality. Further, to obtain the forgetting and retention behavior of the memristive devices, a modified window function equation is proposed by incorporating two novel internal state variables in the form of forgetting rate and retention. The obtained results confirm that the effect of variation in electrical stimuli on forgetting and retention as similar to the biological brain. Therefore, the developed analytical memristive model further can be utilized in the memristive system to develop real-world applications in neuromorphic domains.

Switching motion analysis and synchronization control of a locally active memristive neuron model

“…The phenomenon of ion migration in the memristor is comparable to the diffusion procedure of neurotransmitters at the synapse, so it has become a research hotspot to simulate synapses in neurons with memristors [20][21][22][23][24][25][26]. Chen et al [27] propose a memristive HNN and discover diverse discharge modes.…”