Emergent solution based IGZO memristor towards neuromorphic applications

Martins, Raquel Azevedo; Carlos, Emanuel; Deuermeier, Jonas; Pereira, Maria; Martins, Rodrigo; Fortunato, Elvira

doi:10.1039/d1tc05465a

Cited by 31 publications

(20 citation statements)

References 34 publications

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“…4(c) and (e)) were deconvoluted into three Gaussian peaks of suitable full width at half maximum (FWHM) for each peak position, which correspond to the metal-bound oxygen (M-O: In/Ga/Zn/Al-O) centred at around ∼530-531 eV, the oxygen vacancy (V O ) centred at around ∼531-532 eV and the loosely bound oxygen (M : OH or O Ads ) at around ∼532-533 eV. 25,[39][40][41][42][43] Compared to region I, the proportion of V O states is relatively larger in region II. In addition, a slight decrease in the M-O peak is observed in region II due to the diminishing/absence of IGZO in this region.…”

Section: Structural Aspectsmentioning

confidence: 99%

Interface roughness effects and relaxation dynamics of an amorphous semiconductor oxide-based analog resistance switching memory

Haripriya¹,

Noh²,

Lee³

et al. 2023

Nanoscale

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Section: Structural Aspectsmentioning

confidence: 99%

Interface roughness effects and relaxation dynamics of an amorphous semiconductor oxide-based analog resistance switching memory

Haripriya¹,

Noh²,

Lee³

et al. 2023

Nanoscale

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“…The amorphous oxide semiconductor (AOS), indium gallium zinc oxide (IGZO) has also been demonstrated as active layer for memristors due to its great potential for 1T1M integration. [28][29][30][31][32][33][34][35][36] IGZO is often employed on TFTs in the display technology [37,38] as the semiconductor layer due to its excellent stability, cost-efficient manufacturing, and the additional control on its electrical conductivity provided by the oxygen and cation composition. [39] In fact, the development of neuromorphic displays is the unavoidable new trend for system-on-panel applications.…”

Section: Introductionmentioning

confidence: 99%

Flexible Active Crossbar Arrays Using Amorphous Oxide Semiconductor Technology toward Artificial Neural Networks Hardware

Pereira

Deuermeier

Figueiredo

et al. 2022

Adv Elect Materials

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Memristor crossbar arrays can compose the efficient hardware for artificial intelligent applications. However, the requirements for a linear and symmetric synaptic weight update and low cycle‐to‐cycle (C2C) and device‐to‐device variability as well as the sneak‐path current issue have been delaying its further development. This study reports on a thin‐film amorphous oxide‐based 4×4 1‐transistor 1‐memristor (1T1M) crossbar. The a‐IGZO crossbar is built on a flexible polyimide substrate, enabling IoT and wearable applications. In the novel framework, the thin‐film transistor and memristor are fabricated at the same level, with the same processing steps and sharing the same materials for all layers. The 1T1M cells show linear and symmetrical plasticity characteristic with low C2C variability. The memristor performs like an analog dot product engine and vector–matrix multiplications in the 4×4 crossbars is demonstrated experimentally, in which the sneak‐path current issue is successfully suppressed, resulting in a proof‐of‐concept for a cost‐effective, flexible artificial neural networks hardware.

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“…Some researchers analyzed the impact of activation function, the number of hidden layers and filters on conductance drift [16]. Bayesian neural network (BNN) constructs the model based on a probabilistic perspective, and its parameters are not fixed but are subject to distributions [17]. The original intention of BNN is to improve the robustness and interpretability of the model [18].…”

Section: Introductionmentioning

confidence: 99%

“…[49] designed an end-to-end spatio-temporal network PhysNet, based on which two kinds of spatio-temporal convolutions, i.e., 3D convolution and long short term memory (LSTM), are embedded. Huang et al [17] proposed PRNet, which first employs 3D convolution modules for spatial and local temporal feature extraction, followed by LSTM modules for global temporal feature extraction. Hu et al [14] proposed ETA-rPPGNet, in which a time-domain attention mechanism is designed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time System Predictive Analysis for Heart Rate Pathologies using Machine Learning

Wang¹,

Cheng²,

qijun³

2022

Preprint

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We develop statistical algorithms to infer possiblecardiac pathologies, based on data collected from 24h Holterrecording over a sample of 2829 labelled patients; labels highlightwhether a patient is sufering from cardiac pathologies. In the frstpart of the work we analyze statistically the heart-beat seriesassociated to each patient and we work them out to get a coarse-grained description of heart variability in terms of 49 markerswell established in the reference community. These markers arethen used as inputs for a multi-layer feed-forward neural networkthat we train in order to make it able to classify patients.However, before training the network, preliminary operations arein order to check the efective number of markers (via principalcomponent analysis) and to achieve data augmentation (becauseof the broadness of the input data). With such groundwork,we fnally train the network and show that it can classify withhigh accuracy (at most 85patients that are healthy from thosedisplaying atrial fbrillation or congestive heart failure. In thesecond part of the work, we still start from raw data and we geta classifcation of pathologies in terms of their related networks:patients are associated to nodes and links are drawn accordingto a similarity measure between the related heart-beat series.We study the emergent properties of these networks lookingfor features (e.g., degree, clustering, clique proliferation) ableto robustly discriminate between networks built over healthypatients or over patients sufering from cardiac pathologies. Wefnd overall very good agreement among the two paved routes.

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Emergent solution based IGZO memristor towards neuromorphic applications

Abstract: Solution-based memristors are emergent devices, due to their potential in electrical performance for neuromorphic computing combined with simple and cheap fabrication processes. However, to reach a practical application in crossbar...

Cited by 31 publications

References 34 publications

Interface roughness effects and relaxation dynamics of an amorphous semiconductor oxide-based analog resistance switching memory

Interface roughness effects and relaxation dynamics of an amorphous semiconductor oxide-based analog resistance switching memory

Flexible Active Crossbar Arrays Using Amorphous Oxide Semiconductor Technology toward Artificial Neural Networks Hardware

Real-Time System Predictive Analysis for Heart Rate Pathologies using Machine Learning

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