Optically Tunable Electrical Oscillations in Oxide‐Based Memristors for Neuromorphic Computing

Nath, Shimul Kanti; Das, Sujan Kumar; Nandi, Sanjoy Kumar; Xi, Chen; Marquez, Camilo Verbel; Rúa, Armando; Uenuma, Mutsunori; Wang, Zhongrui; Zhang, Songqing; Zhu, Rui‐Jie; Eshraghian, Jason; Sun, Xiao; Lu, Teng; Bian, Yue; Syed, Nitu; Pan, Wenwu; Wang, Han; Lei, Wen; Fu, Lan; Faraone, Lorenzo; Liu, Yun; Elliman, Robert G.

doi:10.1002/adma.202400904

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…The structure, composition, and morphology of the films were subsequently analyzed using grazing incidence X-ray diffraction (GI-XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) (see Supporting Information). 18,44 Rietveld refinement was used to determine the positions, heights, and widths of XRD peaks using the Fullprof software package. 45,46 Electrical measurements were performed in air using an Agilent B1500A semiconductor parametric analyzer attached to a Signatone probe station with a heating-controlled sample stage.…”

Section: Experimental Methodsmentioning

confidence: 99%

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation

Bonagiri,

Das,

Marquez

et al. 2024

ACS Appl. Mater. Interfaces

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Neuromorphic nanoelectronic devices that can emulate the temperature-sensitive dynamics of biological neurons are of great interest for bioinspired robotics and advanced applications such as in silico neuroscience. In this work, we demonstrate the biomimetic thermosensitive properties of two-terminal V 3 O 5 memristive devices and showcase their similarity to the firing characteristics of thermosensitive biological neurons. The temperature-dependent electrical characteristics of V 3 O 5 -based memristors are used to understand the spiking response of a simple relaxation oscillator. The temperature-dependent dynamics of these oscillators are then compared with those of biological neurons through numerical simulations of a conductance-based neuron model, the Morris−Lecar neuron model. Finally, we demonstrate a robust neuromorphic thermosensation system inspired by biological thermoreceptors for bioinspired thermal perception and representation. These results not only demonstrate the biorealistic emulative potential of threshold-switching memristors but also establish V 3 O 5 as a functional material for realizing solid-state neurons for neuromorphic computing and sensing applications.

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Section: Experimental Methodsmentioning

confidence: 99%

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation

Bonagiri,

Das,

Marquez

et al. 2024

ACS Appl. Mater. Interfaces

Self Cite

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Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision

Ahsan,

Chae,

Jalal

et al. 2024

ACS Nano

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In-sensor and near-sensor computing architectures enable multiply-accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-todigital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply-accumulate operations as the sensor acquires data. We implement in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. We introduce a computing scheme based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, non-linear operations on the inputs. Simulation elucidates how this computing occurs. We experimentally implement a 3×3 focal plane array of coupled neurons and show that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3×3 array of coupled neurons feeding into a

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Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system

Li,

Tang

et al. 2024

Nat Commun

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Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.

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Optically Tunable Electrical Oscillations in Oxide‐Based Memristors for Neuromorphic Computing

Cited by 4 publications

References 48 publications

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision

Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system

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