Ultra-low power Hf_0.5Zr_0.5O₂ based ferroelectric tunnel junction synapses for hardware neural network applications

Abstract

Brain-inspired neuromorphic computing has shown great promise beyond the conventional Boolean logic. Nanoscale electronic synapses, which have stringent demands for integration density, dynamic range, energy consumption, etc., are key computational elements of the brain-inspired neuromorphic system. Ferroelectric tunneling junctions have been shown to be ideal candidates to realize the functions of electronic synapses due to their ultra-low energy consumption and the nature of ferroelectric tunneling. Here, we report a new electronic synapse based on a three-dimensional vertical Hf0.5Zr0.5O2-based ferroelectric tunneling junction that meets the full functions of biological synapses. The fabricated three-dimensional vertical ferroelectric tunneling junction synapse (FTJS) exhibits high integration density and excellent performances, such as analog-like conductance transition under a training scheme, low energy consumption of synaptic weight update (1.8 pJ per spike) and good repeatability (>103 cycles). In addition, the implementation of pattern training in hardware with strong tolerance to input faults and variations is also illustrated in the 3D vertical FTJS array. Furthermore, pattern classification and recognition are achieved, and these results demonstrate that the Hf0.5Zr0.5O2-based FTJS has high potential to be an ideal electronic component for neuromorphic system applications.

Memristive Synapses for Brain‐Inspired Computing

“…Synaptic functions have also been implemented in ferroelectric tunnel memristors based on Pt/BaTiO 3 /Nb:SrTiO 3 and Ag/PbZr 0.52 Ti 0.48 O 3 /La 0.8 Sr 0.2 MnO 3 ferroelectric tunnel junctions . In addition, three‐terminal ferroelectric synapses have also been developed by using Pb(Zr,Ti)O 3 and Hf 0.5 Zr 0.5 O 2 based ferroelectric tunnel junctions …”

Recent Progress in Synaptic Devices Based on 2D Materials

“…Various synaptic devices based on resistive switching, driven by different physical working mechanisms such as active metallic filament, charge trapping/detrapping effect, ions/vacancies migration, phase change behaviors, ferroelectric polarization, and spin‐transfer torque‐based synapses, have been demonstrated for emerging memory and neuromorphic computing. Many scientists are actively working to resolve various issues in those synaptic devices: high energy consumption, low switching speed, poor reliability, or the lack of high device density for integration.…”

CMOS Compatible Hf0.5Zr0.5O2 Ferroelectric Tunnel Junctions for Neuromorphic Devices

“…Hf 0.5 Zr 0.5 O 2 has the advantage of crystallizing at low temperature, compatible with the CMOS process, while displaying ferroelectricity at very low thicknesses due to the presence of an orthorhombic phase . While Chen and coworkers has proposed Hf 0.5 Zr 0.5 O 2 FTJs for bio‐inspired artificial synapses, quantitative experimental verification is yet to be demonstrated. Spike‐timing‐dependent plasticity (STDP) tests are the technique of choice to evaluate the material's ability to mimic synaptic plasticity .…”

“…Spike‐timing‐dependent plasticity (STDP) tests are the technique of choice to evaluate the material's ability to mimic synaptic plasticity . STDP emulates the behavior of the brain's neuronal activity, where two neurons stimulate the synapse interconnecting them by each applying a voltage pulse . This pulse is also referred to as action potential and the time difference between the two action potentials defines the strengthening or the weakening of the synapse, and therefore the resistive switching in FTJs.…”

“…The learning rate is an important parameter for the training of an ANN and to a great extent influences its training algorithm. While STDP has already been employed to evaluate RRAM's learning behavior, a quantitative study of describing the influence of the action potential on the learning rate of the synapse has yet to be reported.…”