Neuro-Mimetic Dynamics of a Ferroelectric FET-Based Spiking Neuron

Fang, Yan; Gomez, Jorge; Wang, Zheng; Datta, Suman; Khan, Asif Islam; Raychowdhury, Arijit

doi:10.1109/led.2019.2914882

Cited by 47 publications

(36 citation statements)

References 17 publications

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“…More importantly, this neuron model is capable of modeling multiple neural dynamics that has been observed in cortical and thalamic neurons. We can use two gate voltages, V GM and V GF , of two transistors to imitate the excitatory and inhibitory synaptic inputs, respectively of biological neurons, and thus enable various neural firing patterns (Fang et al, 2019). In this section, we describe a compact behavior model of the FeFET based spiking neuron.…”

Section: Methodsmentioning

confidence: 99%

“…We focus our model on the critical voltages when FeFET switches and the current that charges and discharges the capacitors. Details of the model have been presented elsewhere (Fang et al, 2019) and we summarize the key findings here for the sake of completion. It is also important to point out the key neuronal dynamics that are achievable in the FeFET neuron, that can be harnessed in the SI-SNN computational framework.…”

Section: Methodsmentioning

confidence: 99%

“…To capture V t 1 and V t 2 , we only need to aim at the boundary conditions when the FeFET switches. Thus, we can write the equation based on charge (Fang et al, 2019):…”

Section: Methodsmentioning

confidence: 99%

“…V fe is the potential across the FE layer and equals to one of the two coercive voltages, V c 1 and V c 2 . Therefore, we can compute the critical voltages of switching, V t 1 and V t 2 as (Fang et al, 2019):…”

Section: Methodsmentioning

confidence: 99%

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A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

et al. 2019

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As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In this work, we explore the feasibility of connecting these two models by implementing a generalized SI model on SNN. In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing. The coupled neurons communicate and modulate each other's action potentials through event-driven spikes and synchronize their dynamics around the states of optimal solutions. We demonstrate that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions. Furthermore, we demonstrate an efficient implementation of such neural dynamics on an emerging hardware platform, namely ferroelectric field-effect transistor (FeFET) based spiking neurons. Such an emerging in-silico neuron is composed of a compact 1T-1FeFET structure with both excitatory and inhibitory inputs. We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…To capture V t 1 and V t 2 , we only need to aim at the boundary conditions when the FeFET switches. Thus, we can write the equation based on charge (Fang et al, 2019):…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

et al. 2019

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“…This three-terminal device has recently been operated into memory arrays with 28 nm CMOS technology [114] and exhibits a strong potential for the development of 3D structures [115]. Also, it has been operated to replicate synapse [116] and neuron [117,118] functions, which, combined with 3D integration opportunity, makes it a strong candidate for neuromorphic computing applications. Figure 12b illustrates the device structure of the ECRAM consisting of an MOS transistor where a solid-state electrolyte based on inorganic materials, such as lithium phosphorous oxynitride (LiPON) [108,119], or organic materials, such as poly (3, 4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) [120], is used as the gate dielectric.…”

Section: Memristive Devices With Three-terminal Structurementioning

confidence: 99%

Memristive and CMOS Devices for Neuromorphic Computing

et al. 2020

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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

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Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

Gong,

Wei,

Wang

et al. 2024

Adv Funct Materials

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Neuromorphic devices capable of emulating biological synaptic behaviors are crucial for implementing brain‐like information processing and computing. Emerging 2D ferroelectric neuromorphic devices provide an effective means of updating synaptic weight aside from conventional electrical/optical modulations. Here, by further synergizing with an energy‐efficient synaptic plasticity strategy, a multimodal mechano‐photonic synaptic memory device based on 2D asymmetric ferroelectric heterostructure is presented, which can be modulated by external mechanical behavior and light illumination. By integrating the asymmetric ferroelectric heterostructured field‐effect transistor and a triboelectric nanogenerator, the mechanical displacement‐derived triboelectric potential is ready for gating, programming, and plasticizing the synaptic device, resulting in superior electrical properties of high on/off ratios (> 107), large storage windows (equivalent to ≈95 V), excellent charge retention capability (> 104 s), good endurance (> 103 cycles), and primary synaptic behaviors. Besides, optical illumination can effectively synergize with mechanoplasticity to implement multimodal spatiotemporally correlated dynamic logic. The demonstrated multimodal memory synapse provides a facile and promising strategy for multifunctional sensory memory, interactive neuromorphic devices, and future brain‐like electronics embodying artificial intelligence.

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Neuro-Mimetic Dynamics of a Ferroelectric FET-Based Spiking Neuron

Cited by 47 publications

References 17 publications

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Memristive and CMOS Devices for Neuromorphic Computing

Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

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