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The success of artificial neural networks (ANNs) in machine vision techniques has driven hardware researchers to explore more efficient computing elements for energy-expensive operations such as vector-matrix multiplication (VMM). In this work, InP-based floating-gate photo-field-effective transistors (FG-PFETs) are demonstrated as computing elements that integrate both photodetection and initial signal processing at the sensor level. These devices are fabricated from semiconductor channels grown via a back-end CMOS compatible templated liquid phase (TLP) approach. Individual devices are shown to exhibit programmable responsivity, mimicking the effect of a synapse connecting the photodetector to a neuron. Using these devices, a simulated optical neural network (ONN) where the experimentally measured performance of FG-PFETs is used as an input shows excellent image recognition accuracy for color-mixed handwritten digits.
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(Bi0.2Sb0.8)2Te3 based dynamic synapses with programmable spatio-temporal dynamics
Neuromorphic computing has recently emerged as a promising paradigm to overcome the von-Neumann bottleneck and enable orders of magnitude improvement in bandwidth and energy efficiency. However, existing complementary metal-oxide-semiconductor (CMOS) digital devices, the building block of our computing system, are fundamentally different from the analog synapses, the building block of the biological neural network—rendering the hardware implementation of the artificial neural networks (ANNs) not scalable in terms of area and power, with existing CMOS devices. In addition, the spatiotemporal dynamic, a crucial component for cognitive functions in the neural network, has been difficult to replicate with CMOS devices. Here, we present the first topological insulator (TI) based electrochemical synapse with programmable spatiotemporal dynamics, where long-term and short-term plasticity in the TI synapse are achieved through the charge transfer doping and ionic gating effects, respectively. We also demonstrate basic neuronal functions such as potentiation/depression and paired-pulse facilitation with high precision (>500 states per device), as well as a linear and symmetric weight update. We envision that the dynamic TI synapse, which shows promising scaling potential in terms of energy and speed, can lead to the hardware acceleration of truly neurorealistic ANNs with superior cognitive capabilities and excellent energy efficiency.
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- PAR ID:
- 10595168
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Materials
- Volume:
- 7
- Issue:
- 10
- ISSN:
- 2166-532X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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