In recent years, biologically inspired systems, which emulate the nervous system of living beings, are becoming more and more requested due to their ability to solve ill-posed problems such as pattern recognition or interaction with the external environment. By virtue of their nanoscaled size and their tunable conductance, memristors are key elements to emulate high-density networks of biological synapses that regulate the communication efficacy among neurons and implement learning capability. We propose a TiN/ HfO2/Ti/TiN memristor as artificial synapse for neuromorphic architectures. The device can gradually change its conductance upon application of proper electrical stimuli. More specifically, it features gradual potentiation and depression when stimulated by trains of identical potentiating or depressing spikes, which are easy to be implemented on-chip. Moreover, we demonstrate that the memristor conductance can be regulated according to the delay time between two spikes incoming to the device terminals. This regulation of memristor conductance implements the typical biological learning process named Spike-Time-Dependent-Plasticity (STDP). Finally, collected STDP data were used to simulate a simple fully connected Spiking Neural Network (SNN) for pattern recognition.
Covi, E., Brivio, S., Serb, A., Prodromakis, T., Fanciulli, M., Spiga, S. (2016). HfO2-based memristors for neuromorphic applications. In Proceedings - IEEE International Symposium on Circuits and Systems (pp.393-396). Institute of Electrical and Electronics Engineers Inc. [10.1109/ISCAS.2016.7527253].
HfO2-based memristors for neuromorphic applications
Fanciulli M.;
2016
Abstract
In recent years, biologically inspired systems, which emulate the nervous system of living beings, are becoming more and more requested due to their ability to solve ill-posed problems such as pattern recognition or interaction with the external environment. By virtue of their nanoscaled size and their tunable conductance, memristors are key elements to emulate high-density networks of biological synapses that regulate the communication efficacy among neurons and implement learning capability. We propose a TiN/ HfO2/Ti/TiN memristor as artificial synapse for neuromorphic architectures. The device can gradually change its conductance upon application of proper electrical stimuli. More specifically, it features gradual potentiation and depression when stimulated by trains of identical potentiating or depressing spikes, which are easy to be implemented on-chip. Moreover, we demonstrate that the memristor conductance can be regulated according to the delay time between two spikes incoming to the device terminals. This regulation of memristor conductance implements the typical biological learning process named Spike-Time-Dependent-Plasticity (STDP). Finally, collected STDP data were used to simulate a simple fully connected Spiking Neural Network (SNN) for pattern recognition.
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