The behaviour and peculiarities of a ZnO/Tantalum oxide Memristor system.
M. M. De Souza and P. B. Pillai.
EEE Department,The University of Sheffield,UK S37HQ.
Email:
Neuromorphic computers have architectures that can mimic a human brain with billions of neurons and
their synapses to store, retrieve and process information using future chip technology. One of the driving
forces of neuromorphic computing is the requirement for low energy consumption, as present day chips
still consume tens if not hundreds of watts for a relative small fraction of tasks capable in a human brain.
Neurons are either digital or analogue and can work asynchronously. However, the power performance and
density of synapses is of critical importance as they far exceed neurons in number. Two terminal
memristor devices such as (Resistive Ram RERAM, Conductive Bridge Memory (CB RAM), Phase
Change Memory (PCM) and ferroelectric Memory (FeRAM) have been shown to have excellent properties
for non-volatile RAM applications that mimic the properties of synapses. [1-4]. In biological systems,
signal transmission and learning process occurs concurrently and the learning process is via a change in
synaptic weight. The present two terminal synaptic devices that function as a connecting element between
a pre and post neuron terminals, have the limitation that signal transmission and learning functions cannot
be carried out simultaneously. The learning function in two terminal devices are carried out by feeding the
signal from post neuron to the synaptic device terminal to modulate the synaptic weight while the synaptic
transmission is inhibited. However, three terminal synaptic devices are able to realise both signal
transmission and learning functions, where signal transmission is carried via the channel medium and
synaptic weights are modulated independently via the gate terminal. Of the three terminal based synaptic
devices, Ferroelectric FETs (FeFETs) and ion gated FETs/TFTs are the most discussed [4-9]. Oxide based
memristors in recent years have reported properties of signal transmission and self-learning: at the heart of
their mechanism lies a motion of positively charged oxygen vacancies that have been widely examined in
the context of stability and performance of Transparent oxide thin film transistors (TFTs). In this talk, we
will present the latest developments that summarise and compare the performance of a ZnO/Ta
2
O
5
based
memristor “system” with other materials reported to date. The synaptic functions are emulated at a low
programming voltage of 200 mV, an order of magnitude smaller than in conventional RERAM. The
synaptic behaviour of our transistors is found to be independent of environmental factors. By the
application of relatively higher pre-synaptic spike signal at the gate terminal, a long term memory retention
lasting up to several (>3) hours is observed. We will present data analysis that explains the relaxation
kinetics of the oxygen vacancies in these samples and draw conclusions on possible mechanisms related to
observed synaptic and transistor characteristics.
References:
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(2005).
[4]
Hiroshi Ishiwara , “Proposal of Adaptive-Learning Neuron Circuits with Ferroelectric Analog-Memory Weights”,
Jpn. J. Appl. Phys. 32 442 (1993)
[5]
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