Performing hardness classification using diffusive memristor based artificial neurons

Abstract Artificial neurons and synapses are the building blocks for constructing a neuromorphic system such as Spiking Neural Network (SNN) or Artificial Neural Network (ANN). Recently, there has been tremendous interest in using memristors to develop neuromorphic technologies that can be used in advanced SNNs and ANNs. Memristors, because of their simple device structure, easy and high-density fabrication, and integration with other semiconductor electronics are suitable candidates for the construction of neuromorphic concepts. However, not much has been discussed about using memristors for the development of sensors that can be utilized for object- classification especially their rigidity, shape and structure. In this article, we propose the application of memristors, specifically silver nanoparticle based diffusive memristor, in conjunction with a piezoelectric sensor within a robotics gripper, serving as one receptor (a tactile sensor) that triggers neuron circuitry with memristors to generate spikes. Furthermore, to perform hardness classification, we utilized various objects to collect data and generated multiple spikes corresponding to each object. This data was then utilized with a machine learning algorithm. The outcomes were compared with the accuracy of commercial FSR (force-sensing resistor) tactile sensors. Our approach demonstrated the capability of diffusive memristors in generating neuron spikes from tactile stimuli for hardness classification, achieving accuracy ranging from 82% to 100% during the validation of 20% test data across various algorithms, while the FSR sensors achieved an accuracy range of 95% to 98%.