Adhesive effect of electric fields across cracks

Kendall, K.

doi:10.1088/0022-3727/24/7/006

Cited by 13 publications

(17 citation statements)

References 9 publications

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“…[ 1–3 ] One potential concept to overcome this so‐called von Neumann bottleneck for certain applications is the development of neuromorphic computing architectures, which aim to emulate information processing in the human brain. [ 4–7 ] In biology, information processing takes place in huge networks of neurons and synapses, without physical separation between computation and memory, [ 8 ] leading to an impressive performance in tasks like sensory processing, motor control, and pattern recognition, [ 9 ] while at the same time consuming less energy, orders of magnitude lower than digital computers require to conduct similar tasks. [ 5,6,10,11 ]…”

Section: Introductionmentioning

confidence: 99%

Trap‐Assisted Memristive Switching in HfO₂‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Zahari

Marquardt

Kalläne

et al. 2023

Adv Elect Materials

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Memristive devices are under intense development as non‐volatile memory elements for extending the computing capabilities of traditional silicon technology by enabling novel computing primitives. In this respect, interface‐based memristive devices are promising candidates to emulate synaptic functionalities in neuromorphic circuits aiming to replicate the information processing of nervous systems. A device composed of Nb/NbOx/Al2O3/HfO2/Au that shows promising features like analog switching, no electro‐forming, and high current‐voltage non‐linearity is reported. Synchrotron‐based X‐ray photoelectron spectroscopy and depth‐dependent hard X‐ray photoelectron spectroscopy are used to probe in situ different resistance states and thus the origin of memristive switching. Spectroscopic evidence for memristive switching based on the charge state of electron traps within HfO2 is found. Electron energy loss spectroscopy and transmission electron microscopy support the analysis. A device model is proposed that considers a two‐terminal metal–insulator–semiconductor structure in which traps within the insulator (HfO2/Al2O3) modulate the space charge region within the semiconductor (NbOx) and, thereby, the overall resistance. The experimental findings are in line with impedance spectroscopy data reported in the companion paper (Marquardt et al). Both works complement one another to derive a detailed device model, which helps to engineer device performance and integrate devices into silicon technology.

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Section: Introductionmentioning

confidence: 99%

Trap‐Assisted Memristive Switching in HfO₂‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Zahari

Marquardt

Kalläne

et al. 2023

Adv Elect Materials

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“…Traditional von Neumann based computing architecture is inefficient and energy‐hungry for such data‐driven tasks. Non‐volatile IMC technique becomes an ideal choice for machine learning because synaptic weights can be mapped to 2D memory crossbar arrays to perform parallel computing with the input signal, [ 42 ] which greatly improves the processing efficiency of the hardware system. Different neural network models such as convolutional neural networks, [ 27,43 ] long short‐term memory networks, [ 44 ] deep neural networks, [ 45,46 ] and reservoir computing [ 8 ] have been implemented with IMC architecture.…”

Section: Applications Based On Imc Scenariomentioning

confidence: 99%

“…Another promising application is brain‐inspired computing, [ 42,56 ] which aims to build a brain‐like information processing system that can simultaneously possess both memory and learning capabilities. The human brain is a complicated neural network composed of more than 10 11 neurons and 10 15 synapses.…”

Section: Applications Based On Imc Scenariomentioning

confidence: 99%

Functional Applications of Future Data Storage Devices

Yang

Zhou

Han

2021

Adv Elect Materials

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traditional computing systems. Strategies like adopting multiple levels of cache, [3] increasing memory bandwidth, [4] and near-memory computing [5] have been proposed to alleviate this problem which yet still seem to be insufficient to meet the increasing computing demands.In-memory computing (IMC) which refers to the realization of computational tasks within the memory unit aims to reduce the frequent movement of data across the bus (Figure 1b), providing new insights for building highly efficient computing systems. IMC with function of parallel computation, reducing either the computational complexity or accessed amount of data effectively can be performed on both volatile and non-volatile memories. IMC has a wide range of applications, ranging from stochastic computing that requires randomness and low precision to scientific computing with high precision. Volatile memories including static randomaccess memory (SRAM) and dynamic random-access memory (DRAM) have been mainly utilized to execute Boolean functions and obtained dozens of times speedup in compute-heavy as well as low input-output movement applications compared with conventional architectures in which SRAM and DRAM were only used for data storage. [6] While nonvolatile memory including Flash and emerging memristive devices (resistive random-access memory [RRAM], phase-change memory [PCM], and magnetic random-access memory [MRAM], etc.) with the ability to store a continuous conductance state were proved to perform matrix-vector multiplication (MVM) for parallel IMC based on Ohm's law and Kirchhoff's law. [7] Especially, the memristive device with crossbar configuration can be directly integrated with high density local interconnects and low thermal burden, allowing the significant boost to IMC performance.This review provides an overview of related work in various memory techniques, their applications, and required parameters in IMC, the current state of the art, and evaluation metrics and highlights opportunities for further development. The Development of Memory TechnologiesDepending on the state of the data being stored, current memory technologies fall into two categories (Figure 2a): volatile and nonvolatile. Volatile memories will lose the data preserved when the power is disconnected, mainly including SRAM and DRAM, while information in nonvolatile memories including read-only memory (ROM), hard disk driver (HDD), Flash, MRAM, PCM, RRAM, and ferroelectric random-accessThe development of artificial intelligence and big data analytics is driving a revolution in methods for data processing and storage. Confronting speed and energy consumption issues, these fields require new computing systems to parallelly retrieve, process, and store massive amounts of data. Beyond CMOS devices and technology, advances in data storage technology such as static random-access memory, dynamic random-access memory, flash memories, resistive memories, phase change memories, and magnetic memories have made functional computing possible. In this article, a broad overvi...

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“…In this circumstance, the integration of multifunctions in a single device, such as in‐memory photodetectors, is regarded as an ideal solution to this problem. [ 7–10 ] These devices can not only dramatically simplify the conventional image sensor circuitry, but also be applied as a wide variety of photonic synaptic devices for neuromorphic computing and objects recognition in a complex environment. [ 11,12 ] Accordingly, it is necessary to explore novel materials and innovative device structures in photodetection with integrated working modes, which can accommodate the explosive data flows.…”

Section: Introductionmentioning

confidence: 99%

A High‐Performance In‐Memory Photodetector Realized by Charge Storage in a van der Waals MISFET

Zhang

Chen

et al. 2022

Advanced Materials

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explosive increase in data information, however, significantly challenges the conventional von Neumann architecture in which sensing, memory, and computing functions are realized by discrete devices. In this circumstance, the integration of multifunctions in a single device, such as in-memory photodetectors, is regarded as an ideal solution to this problem. [7][8][9][10] These devices can not only dramatically simplify the conventional image sensor circuitry, but also be applied as a wide variety of photonic synaptic devices for neuromorphic computing and objects recognition in a complex environment. [11,12] Accordingly, it is necessary to explore novel materials and innovative device structures in photodetection with integrated working modes, which can accommodate the explosive data flows. [13] Among numerous novel materials, 2D materials are promising type of media for the evolution of in-memory photodetection. [14][15][16] On the one hand, 2D-material-based flash memories demonstrate ultrafast (nano seconds) operation speeds and long-term non-volatile property (10 years), owing to the atomically sharp interface and optimized barrier height. [17][18][19] On the other hand, their superior optoelectronic characteristics make them ideal photodetecting materials, considering their unique features of absences of dangling bonds [20][21][22] and tunable bandgaps. [23][24][25] Regarding the current research into 2D-material-based highperformance in-memory photodetectors, charge-storage effect is a practical approach. [15] Different from the working flow of normal photo detectors, this kind of in-memory photodetectors generally photosensitize at a positive or negative gate voltage to realize non-volatile charge writing and erasing. [26,27] In this vein, a few attempts at achieving charge storage have been carried out, which however yielded unsatisfactory photodetection or memory performances. [27][28][29][30] For instance, the MoS 2 / functionalized SiO 2 device with interface charge trap yields low readout photocurrent (<10 nA) and short non-volatile retention characteristic (≈20 s). [27] The floating gate structure of MoS 2 /h-BN/MoS 2 on Si/SiO 2 substrate showed longer retention time (>10 4 s) but suffers from the weakness of low readout photocurrent (only ≈100 nA). [28] Furthermore, the chargestorage mechanisms of almost all in-memory photo detectors are only at the hypothesis stage, lacking direct experimentalThe emerging data-intensive applications in optoelectronics are driving innovation toward the fused integration of sensing, memory, and computing to break through the restrictions of the von Neumann architecture. However, the present photodetectors with only optoelectronic conversion functions cannot satisfy the growing demands of the multifunctions required in single devices. Here, a novel route for the integration of non-volatile memory into a photodetector is proposed, with a WSe 2 /h-BN van der Waals heterostructure on a Si/SiO 2 substrate to realize in-memory photodetection. This photodetector exhibits a...

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Adhesive effect of electric fields across cracks

Cited by 13 publications

References 9 publications

Trap‐Assisted Memristive Switching in HfO₂‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Trap‐Assisted Memristive Switching in HfO₂‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Functional Applications of Future Data Storage Devices

A High‐Performance In‐Memory Photodetector Realized by Charge Storage in a van der Waals MISFET

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Adhesive effect of electric fields across cracks

Cited by 13 publications

References 9 publications

Trap‐Assisted Memristive Switching in HfO2‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Trap‐Assisted Memristive Switching in HfO2‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Functional Applications of Future Data Storage Devices

A High‐Performance In‐Memory Photodetector Realized by Charge Storage in a van der Waals MISFET

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Product

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Trap‐Assisted Memristive Switching in HfO₂‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy

Trap‐Assisted Memristive Switching in HfO₂‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy