Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device

Abstract

Efforts to develop scalable learning algorithms for implementation of networks of spiking neurons in silicon have been hindered by the considerable footprints of learning circuits, which grow as the number of synapses increases. Recent developments in nanotechnologies provide an extremely compact device with low-power consumption.In particular, nanoscale resistive switching devices (resistive random-access memory (RRAM)) are regarded as a promising solution for implementation of biological synapses due to their nanoscale dimensions, capacity to store multiple bits and the low energy required to operate distinct states. In this paper, we report the fabrication, modeling and implementation of nanoscale RRAM with multi-level storage capability for an electronic synapse device. In addition, we first experimentally demonstrate the learning capabilities and predictable performance by a neuromorphic circuit composed of a nanoscale 1 kbit RRAM cross-point array of synapses and complementary metal-oxide-semiconductor neuron circuits. These developments open up possibilities for the development of ubiquitous ultra-dense, ultra-low-power cognitive computers.

Simulation Study on Silicon-Based Floating Body Synaptic Transistor with Short- and Long-Term Memory Functions and Its Spike Timing-Dependent Plasticity

“…It is much simpler compared with neuromorphic system using the memristor as a synaptic device. This is because the memristor is a two-terminal device and requires enormous switching component to receive signals from post-synaptic neuron circuits [8][9][10]. STDP characteristics were emulated with time difference (∆t) between pre-and post-synaptic spikes.…”

“…Recently, there are great interests in realizing artificial synapses based on the Hebbian learning for the brain-like computing architectures [1][2][3][4][5][6][7][8][9][10]. This is because a synapse is believed to make a great contribution to many cognitive functions such as perception and memory in a biological system [11][12][13][14][15][16].…”

“…This is because a synapse is believed to make a great contribution to many cognitive functions such as perception and memory in a biological system [11][12][13][14][15][16]. However, one of the strong candidates for artificial synapse, the memristor, is a two terminal device and needs extra selection components to interact with both pre-and post-synaptic neurons circuits [8][9][10]. Moreover, memristors are implemented by the non-silicon devices and have difficulty in being cointegrated with the conventional complementary metaloxide-semiconductor (CMOS) architectures.…”

Integrate-and-Fire Neuron Circuit and Synaptic Device using Floating Body MOSFET with Spike Timing-Dependent Plasticity

“…It is a form of plasticity driven by precise spike timing differences between pre-synaptic and post-synaptic spikes [12]. Most of two-terminal device such as memristor, additional transistor is needed for STDP expression [10]. We used four-terminal device for long-term memory and STDP (Fig.…”

“…The memristor is a two-terminal device whose conductance can be modulated by charge through it. As we connect neuron and synapse, the twoterminal memristor device will require additional controller or clock for the implementation of spiketiming-dependent-plasticity (STDP) characteristics [10,11]. If the system uses controller or clock, the system will be synchronized by the clock.…”