Abstract
We experimentally demonstrate ferroelectric-like non-volatile field-effect transistor (NVFET) with the amorphous Al2O3 gate insulator for artificial synapse applications. The switchable polarization (P) is attributed to the voltage modulation of mobile ions in the gate insulator. The ferroelectric-like NVFETs integrated with 3 nm and 6 nm-thick Al2O3 dielectrics demonstrate the capability to mimic various synaptic behaviors including long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), under different types of electrical stimuli to the gate electrode. To verify the application of the ferroelectric-like transistors in the Spike Neural Network (SNN), the online training has been carried out based on the synaptic characteristics of the devices, and a decent accuracy (>80%) is achieved for fixed-amplitude ±3 V/100 ns potentiation/depression pulses.
Highlights
Energy efficiency and parallel information processing make the human brain a model computing system for unstructured data handling [1], [2]
It is clear to see that the P of amorphous Al2O3 capacitor increases slightly with the increase of the Al2O3 thickness, which may be due to the increase in the number of oxygen vacancies
Note that the post synaptic IDS of the device increases with the VGS pulses number, and the synaptic plasticity of the amorphous Al2O3 based synapse can be the transition from short-term memory (STM) to long-term memory (LTM) by adjusting the input waveform
Summary
Energy efficiency and parallel information processing make the human brain a model computing system for unstructured data handling [1], [2]. The synaptic functions including long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), have been implemented with FeFETs. NVFETs integrated with amorphous gate dielectrics (e.g. Al2O3, ZrO2) were reported and systematically characterized [8]-[12]. The amorphous dielectric NVFETs exhibited significantly better linearity and larger dynamic range for multi-threshold voltage operation [10], having a high potential for low-power neuromorphic devices to closely mimic biological behaviors. The systematic investigation of mimic biological synaptic behavior for amorphous dielectric NVFET and their application in spiking neural network computing has not been explored yet. The accuracy of the network above 80% is achieved based on the spiking neural network (SNN) architecture utilizing a Multi-ReSuMe algorithm [15], [16]
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