(Invited) Electrochemically-Tunable and Low-Power 2D Synapses for Neuromorphic Computing

Abstract

Inspired by the human brain, which is better at complex tasks such as pattern recognition than even supercomputers with much better efficiency, neuromorphic computing has recently attracted much research attention. Biological neural networks employ analog changes in neural connections strength (i.e. synaptic weight) during the decision-making and learning processes, in contrast to modern computers that use digital ‘0’ and ‘1’ for computation. However, majority of the current research efforts towards developing artificial synapses are based on digital technology with the binary SRAM, which consists 8 transistors, making it impractical to scale up the system to the level of complexity we need due to constraints in power and area. Recently, emerging nanoelectronics such as phase-change memory (PCM), oxide-based resistive random access memory (RRAM), metal-ion based electrochemical metallization memory (ECM), spin torque transfer (STT) RAM, ferroelectric FeRAM, and nano-devices have been studied for neuromorphic computing. While some of these approaches especially PCM and RRAM have demonstrated promising results to lower the power consumption and speed up the learning, they still suffer from critical issues such as insufficient precision, non-linear behaviors, and large device variations. In this work, we present a novel approach to build electrochemically-tunable, two-dimensional (2D) synapses with excellent controllability (~0.25% ΔR per switching), good energy efficiency (~500 fJ per switching), symmetric resistance response, and a rare combination of low-power programming and good retention. In our 2D synapses, the channel conductance, representing the synaptic weight in a biological synapse, can be modulated by controlling the concentration of ions between layers of graphene through a process called electrochemical intercalation. The major advantage in our approach is that we can achieve reversible and precise programming of the 2D device’s conductance to mimic synaptic plasticity with low power consumption. Unlike typical memory devices, the programming and storage mechanisms in our electrochemical synapses are decoupled, allowing us to achieve both low-power switching and good retention properties. While electrochemical intercalation has been widely used in rechargeable Li ion batteries (LIBs), this is the first demonstration of a 2D electrochemical analog memory for synaptic devices. We choose graphene in this work because graphite is a common anode material for Li ion batteries (LIBs), with good compatibility and well-characterized behaviors for Li intercalation, as illustrated in our galvanostatic discharge and in-situ Raman measurements. Through tuning the electrochemical potential of graphene, we can modulate the Li concentration and thus the electrical conductance of graphene in a continuous fashion. We perform pulse measurements (50-pA, 10-ms) to emulate the excitatory and inhibitory behavior of synapse, where we achieve ~0.25% change of channel resistance ΔR per pulse. This decrease (increase) in resistance, mimicking an increase (decrease) in synaptic weight of an excitatory (inhibitory) synapse, is the result of an increase (decrease) of Li concentration in graphene. We also achieve long-term potentiation and depression (increase and decrease in synaptic weight) through applying a series of intercalating and de-intercalating pulses. We reliably and reversibly program the graphene synapse into over 400 discrete states over our operating range. The resistance response is highly symmetric and linear. Lastly, we demonstrate spike-timing-dependent plasticity (STDP) in our synapse, where the change in resistance depends on the timing difference Δt between the pulses – similar to the biological phenomenon that the effectiveness of repeated learning depends on the duration of the gap between each stimulation. In summary, we develop an electrochemical, 2D synaptic device with low energy efficiency (500 fJ/synaptic event), precise control over synaptic conductance (0.25% ΔR per step), and good cycling performance. We demonstrate basic neuromorphic functionalities such as short-term and long-term plasticity, as well as spike-timing-dependent plasticity. This work can lead to the low-power hardware implementation of neural networks for neuromorphic computing as well as tunable 2D electronics where the material properties can be precisely engineered.