Brain-inspired ferroelectric Si nanowire synaptic device

Abstract

We herein demonstrate a brain-inspired synaptic device using a poly(vinylidene fluoride) and trifluoroethylene (PVDF-TrFE)/silicon nanowire (Si NW) based ferroelectric field effect transistor (FeFET). The PVDF-TrFE/Si NW FeFET structure achieves reliable synaptic plasticity such as symmetrical potentiation and depression, thanks to the reversible dynamics of the PVDF-TrFE permanent dipole moment. The calculated asymmetric ratio of potentiation and depression is as low as 0.41 at the optimized bias condition, indicating a symmetrical synaptic plasticity behavior. Pattern recognition accuracy based on the actual synaptic plasticity data of the synaptic device can be estimated via the CrossSim simulation software. Our simulation result reveals a high pattern recognition accuracy of 85.1%, showing a potential feasibility for neuromorphic systems. Furthermore, the inverter-in-synapse transistor consisting of the Si NW FeFET synapse and resistor connected in series is able to provide energy-efficient logic circuits. A total noise margin [(NMH + NML)/VDD] of 41.6% is achieved, and the power consumption [Ps = VDD(ID,L + ID,H)/2] of the logic-in-synapse transistor is evaluated to be 0.6 µW per logic gate. This study would shed light on the way toward a brain-inspired neuromorphic computing system based on the FeFET synapse device.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

“…Various types of materials such as perovskites, 2D materials, polymers, and fluorite oxides have been found to have ferroelectricity and studied for next-generation memory devices. − The memory effect of the ferroelectric materials is attributed to the switching of electric dipole alignments between an upward and downward direction, driven by electric field. Ferroelectric memories include ferroelectric random-access memory (FeRAM), ferroelectric tunnel junction (FTJ), and ferroelectric transistors.…”

Nanowire-based synaptic devices for neuromorphic computing

“…The device displayed ultralow power consumption. Lee et al had also demonstrated a Si NW ferroelectric FET (FeFET) [50]. The schematic illustration of the Si NW synaptic FeFET is shown in figure 4(a).…”

“…The recognition accuracy reached to 93% under supervised learning. A back-propagation algorithm based three-layered ANN was constructed from Si NW synaptic FeFET [50], as shown in figure 6(b). After training for 40 epochs, the recognition accuracy reached to 85.1% when recognizing a 28 × 28 pixels MNIST image.…”

Dual-Terminal Stimulated Heterosynaptic Plasticity of IGZO Memtransistor with Al2O3/TiO2 Double-Oxide Structure

“…Unlike the von Neumann computing system, the neuromorphic system mimics a neural network of the human brain and has been attracting considerable attention because of the massively parallel processing capability of unstructured big data with extremely low power consumption. − The neural network in the human brain, comprising 100 billion neurons and 100 trillion synapses whose chemical activity results in a change in synaptic strength, processes both learning and memory with a low power consumption of ∼20 W. In this regard, an artificial device to emulate synaptic plasticity should accompany the gradual resistance change by external stimuli. − Accordingly, diverse artificial synapse devices with different structures and working mechanisms have been widely demonstrated, such as two-terminal based resistive random access memory (RRAM), phase-change random access memory (PCRAM), − three-terminal floating gate synaptic transistors, − ferroelectric field-effect transistors (FeFETs), − and ionic synaptic transistors. , Each synapse device has distinct advantages and disadvantages. For instance, two-terminal-based synapse devices typically consume a large amount of energy to update the synaptic weight despite the ease of high integration, whereas three-terminal-based synapse devices occupy a relatively large device area despite precisely controllable conductance changes.…”