Design of Non-volatile Capacitive Crossbar Array for In-Memory Computing

Luo, Yuan-Chun; Lu, Anni; Hur, Jae; Li, Shaolan; Yu, Shimeng

doi:10.1109/imw51353.2021.9439603

Cited by 15 publications

(14 citation statements)

References 8 publications

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“…The simulation was based on a single‐layer perceptron structure in a Modified National Institute of Standards and Technology database (MNIST) dataset digit recognition. Moreover, the energy consumption was recorded to be up to 57.3 times lower than that for a 45 nm 1 transistor–1 magnetic tunnel junction‐based memory device 85,116,132,133 . If the structure of the FeCAP was M/F/S, an additional capacitance variation by the depletion and accumulation layers could have been achieved.…”

Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 98%

“…The butterfly-like C-V curve shifted owing to the asymmetry, as shown in the inset of Figure 1F. 116,131 Zheng et al managed to store seven states in this FeCAP with a 110% On/Off ratio, and successfully simulated pattern recognition with an accuracy of 87%. 131 The simulation was based on a single-layer perceptron structure in a Modified National Institute of Standards and Technology database (MNIST) dataset digit recognition.…”

Section: Two-terminal Devicesmentioning

confidence: 98%

“…In this structure, the asymmetricity can be caused by different interfaces resulting from different oxidation states of the electrodes during the fabrication or different interface state densities caused by a gradient in oxygen vacancies or other defects. The butterfly‐like C – V curve shifted owing to the asymmetry, as shown in the inset of Figure 1F 116,131 . Zheng et al managed to store seven states in this FeCAP with a 110% On/Off ratio, and successfully simulated pattern recognition with an accuracy of 87% 131 .…”

Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 99%

“…It is known that when the polarization and capacitance of the ferroelectric ( C F ) are increased or the series capacitance of the interface layer and semiconductor ( C IF ) are decreased, E dep increases (Equation (1)) 121 . Polarization, C F , and C IF can be modulated by changing the dopant concentration, electrode, or deposition conditions, implying that a favorable leaky behavior can be engineered 116,120,126,130,254,255 …”

Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 99%

“…Moreover, the energy consumption was recorded to be up to 57.3 times lower than that for a 45 nm 1 transistor-1 magnetic tunnel junction-based memory device. 85,116,132,133 If the structure of the FeCAP was M/F/S, an additional capacitance variation by the depletion and accumulation layers could have been achieved. The trade-offs are similar to that previously discussed for FTJs.…”

Section: Two-terminal Devicesmentioning

confidence: 99%

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Neuromorphic devices based on fluorite‐structured ferroelectrics

Lee

Park

Kim

et al. 2022

InfoMat

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A continuous exponential rise has been observed in the storage and processing of the data that may not curtail in the foreseeable future. The required data processing speed and power consumption are restricted by the buses between the logic and memory devices that are characteristic of the von Neumann computing architecture. Bio-mimicking neuromorphic computing has garnered considerable academic and industrial interest to resolve these challenges.Additionally, devices based on emerging nonvolatile memories capable of mimicking the behaviors of synapses and neurons, which are the main elements in biological computing systems (brains), are attracting significant interest from the device community. With the discovery of ferroelectricity in fluorite-structured oxides, such as HfO 2 and ZrO 2 , which are compatible with the state-of-the-art complementary-metal-oxide-semiconductor processes, ferroelectric devices have rapidly evolved as the main direction of these research and development activities. Fundamental science related to fluorite-structured ferroelectrics has been intensively studied over the last decade. At present, the focus is gradually moving to practical applications, including neuromorphic computing and advanced classical processing or memory units in the conventional von Neumann architecture. However, despite its rapid development, the wealth of recent progress in neuromorphic computing devices based on fluorite-structured ferroelectrics has not been reviewed and systemized. This progress report comprehensively reviews and systemizes the recent progress in artificial synaptic and spiking neuron devices for neuromorphic computing based on fluorite-structured ferroelectrics.

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Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 98%

Section: Two-terminal Devicesmentioning

confidence: 98%

Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 99%

Section: Neuromorphic Computing Systems Based On Fluorite‐structured ...mentioning

confidence: 99%

Section: Two-terminal Devicesmentioning

confidence: 99%

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Neuromorphic devices based on fluorite‐structured ferroelectrics

Lee

Park

Kim

et al. 2022

InfoMat

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Capacitive Synaptor with Gate Surrounding Semiconductor Pillar Structure and Overturned Charge Injection for Compute‐in‐Memory

Kim,

Read,

Shon

et al. 2024

Advanced Intelligent Systems

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The newly suggested synapse capacitor (synaptor) in this work has a cross‐point feature, enabling implementation at a feature size of 4F2. This synaptor has a gate surrounding semiconductor pillar (GSSP) structure with overturned charge injection (OCI) scheme to ensure high capacitive memory window. Sentaurus TCAD simulation tools are used to demonstrate the process feasibility and device characteristics. Two important process parameters are optimized to show the best characteristics; overlap height (Hov) and channel pillar height (Hch). An OCI‐GSSP device that has an aspect ratio of 10 and the minimal overlap height shows the highest Con/Coff over 5 in 40 nm wordline and BL pitch. It is the highest value and the smallest unit device size among the capacitive synapses that have been reported up to now. Advantages of scaled OCI‐GSSP devices are appealed through subarray circuit simulation. The subarray composed of OCI‐GSSP synaptor can calculate one vector‐matrix multiplication operation with energy under 200 fJ and column delay of 3 ns, and result in sufficient signal margin of 275 mV.

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Ferroelectric HfO₂-based synaptic devices: recent trends and prospects

Hur

Luo

et al. 2021

Semicond. Sci. Technol.

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Neuro-inspired deep learning algorithms have shown promising futures in artificial intelligence. Despite the remarkable progress in software-based neural networks, the traditional von-Neumann hardware architecture has suffered from limited energy efficiency while facing unprecedented large amounts of data. To meet the performance requirements of neuro-inspired computing, large-scale vector-matrix multiplication is preferred to be performed in situ, namely compute-in-memory. Non-volatile memory devices with different materials have been proposed for weight storage as synaptic devices. Among them, HfO 2 -based ferroelectric devices have attracted great attention because of their low energy consumption, good complementarymetal-oxide-semiconductor (CMOS) compatibility and multi-bit per cell potential. In this review, recent trends and prospects of the ferroelectric synaptic devices are surveyed. First, we present the three-terminal synaptic devices based on the ferroelectric field effect transistor (FeFET), and discuss the switching physics of the intermediate states, the back-end-of-line integration and the 3D NAND architecture design. Then, we introduce a hybrid precision synapse concept that leverages the volatile charges on the gate capacitor of the FeFET and the non-volatile polarization on the gate dielectric of the FeFET. Lastly, we review two-terminal synaptic devices using the ferroelectric tunnel junction (FTJ) and ferroelectric capacitor (FeCAP). The design margins of the crossbar array with FTJ and FeCAP analyzed.

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Design of Non-volatile Capacitive Crossbar Array for In-Memory Computing

Cited by 15 publications

References 8 publications

Neuromorphic devices based on fluorite‐structured ferroelectrics

Neuromorphic devices based on fluorite‐structured ferroelectrics

Capacitive Synaptor with Gate Surrounding Semiconductor Pillar Structure and Overturned Charge Injection for Compute‐in‐Memory

Ferroelectric HfO₂-based synaptic devices: recent trends and prospects

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Design of Non-volatile Capacitive Crossbar Array for In-Memory Computing

Cited by 15 publications

References 8 publications

Neuromorphic devices based on fluorite‐structured ferroelectrics

Neuromorphic devices based on fluorite‐structured ferroelectrics

Capacitive Synaptor with Gate Surrounding Semiconductor Pillar Structure and Overturned Charge Injection for Compute‐in‐Memory

Ferroelectric HfO2-based synaptic devices: recent trends and prospects

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Ferroelectric HfO₂-based synaptic devices: recent trends and prospects