Ultrafast Synaptic Events in a Chalcogenide Memristor

Abstract

Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 105 times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

“…Theoretical studies have suggested STDP can be used to train spiking neural networks (SNNs) in-situ without trading-off their parallelism [9]- [12]. Further, nano-scale memristive devices have demonstrated biologically plausible STDP behavior in several experiments [13]- [17], and therefore have emerged as an ideal candidate for electrical synapses. To this end, hybrid CMOS-memristor analog very-large-scale integrated (VLSI) circuits have been proposed [18]- [22] to achieve dense integration of CMOS neurons and memristors for brain-inspired computing chips by leveraging the contemporary nanometer silicon processing technology.…”

Emulator Circuits and Resistive Switching Parameters of Memristor

“…It is observed from Figure 25 that fully floating memristor emulator consists of a few numbers of MOS transistors and capacitors without using analog multiplier. Furthermore, STDP is experimentally demonstrated in memristive devices [48][49][50].…”

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

“…Similar pulses were used to emulate asymmetric Hebbian learning with other memristor realizations. 26,30 Different pulse shapes are applied to investigate the emulation of input-dependent learning. Note that the pulses to mimic symmetric learning rules are symmetric in time, thus charging and discharging the QDs should not depend on the temporal order of the pulses, but on the absolute value of the time difference.…”

“…Hence, the symmetric and asymmetric learning rules are beneficial for pattern completion and the recalling and storing of temporal sequences of action potentials, respectively. 28,29 The four different learning rules were artificially emulated by varying electrical input signals in chalcogenide 23,30,31 and metal oxide memristors 32 , and by varying optical input signals of metal-sulphide microfibers. 33 We present the emulation of four learning rules with a quantum dot memristor where the conductance change corresponds to charge transfer between quantum dots (QDs) and a two-dimensional electron gas (2-DEG).…”