Effect of the Si Doping Content in HfO<sub>2</sub> Film on the Key Performance Metrics of Ferroelectric FETs

Mulaosmanovic, Halid; Dünkel, Stefan; Kleimaier, Dominik; Kacimi, Amine El; Beyer, Sven; Breyer, Evelyn T.; Mikolajick, Thomas; Slesazeck, Stefan

doi:10.1109/ted.2021.3100005

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…Therefore, hereafter, potentiation/depression is mainly used regardless of the overall neural system type (ANN or SNN). Although linear potentiation/depression using identical pulses is possible, achieving linear potentiation/depression using identical pulses has rarely been reported in ferroelectric materials 75,76 . This can be understood from the general polarization switching mechanism in ferroelectric materials, which can be modeled by the Komologorov–Avrami–Ishibashi (KAI) or NLS models based on domain wall propagation described by the well‐known Merz's law 77–82 .…”

Section: Advantages and Challenges Of Fluorite‐structured Ferroelectr...mentioning

confidence: 99%

“…Another approach to achieve gradual polarization switching has been experimentally demonstrated. By increasing the Si doping content in HfO 2 , the fraction of non‐FE phase increases, which induces inhomogeneity in the E c distribution and enhances polarization switching dispersion in the ferroelectric layer 76 . Similarly, further studies regarding critical nuclei size in fluorite‐structured ferroelectrics are required for increasing the number of ferroelectric domains under same gate size.…”

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

confidence: 99%

“…In contrast with the single FeFET architecture, Sun et al demonstrated a 2T‐1FeFET hybrid synapse for in situ training and inference that showed high symmetric and linear weight updates 225 . Importantly, because the channel conductance of a FeFET‐based synapse can be adjusted by precisely controlling the polarization, research on ferroelectric domain engineering, especially for increasing the number of domains in nanoscale devices, must also be actively conducted 76 …”

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

confidence: 99%

“…Although linear potentiation/depression using identical pulses is possible, achieving linear potentiation/depression using identical pulses has rarely been reported in ferroelectric materials. 75,76 This can be understood from the general polarization switching mechanism in ferroelectric materials, which can be modeled by the Komologorov-Avrami-Ishibashi (KAI) or NLS models based on domain wall propagation described by the well-known Merz's law. [77][78][79][80][81][82] According to the KAI model, the switched polarization fraction (ΔP/2P s ) can be described as being proportional to [1 À exp(Àt/t 0 ) n ], where t 0 and n are characteristic time and the Avrami exponent, respectively.…”

Section: Advantages and Challenges Of Neuromorphic Computing 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: Advantages and Challenges Of Fluorite‐structured Ferroelectr...mentioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

Section: Advantages and Challenges Of Neuromorphic Computing 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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“…Recently, another way to influence the number of intermediate levels has been experimentally verified and builds upon changing the doping content in the FE layer [63]. Figures 4(a)-(c) show that by increasing the Si doping in HfO 2 , the switching transition becomes less steep and the number of intermediate V T states increases, although the same write pulsing scheme is used.…”

Section: Fefet As a Synapsementioning

confidence: 99%

Ferroelectric-based synapses and neurons for neuromorphic computing

Covi

Mulaosmanovic

Max

et al. 2022

Neuromorph. Comput. Eng.

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The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

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Ferroelectric Field Effect Transistors (FeFETs): Advancements, challenges and exciting prospects for next generation Non-Volatile Memory (NVM) applications

Ajayan¹,

Mohankumar²,

Nirmal

et al. 2023

Materials Today Communications

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Effect of the Si Doping Content in HfO₂ Film on the Key Performance Metrics of Ferroelectric FETs

Cited by 9 publications

References 35 publications

Neuromorphic devices based on fluorite‐structured ferroelectrics

Neuromorphic devices based on fluorite‐structured ferroelectrics

Ferroelectric-based synapses and neurons for neuromorphic computing

Ferroelectric Field Effect Transistors (FeFETs): Advancements, challenges and exciting prospects for next generation Non-Volatile Memory (NVM) applications

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Effect of the Si Doping Content in HfO2 Film on the Key Performance Metrics of Ferroelectric FETs

Cited by 9 publications

References 35 publications

Neuromorphic devices based on fluorite‐structured ferroelectrics

Neuromorphic devices based on fluorite‐structured ferroelectrics

Ferroelectric-based synapses and neurons for neuromorphic computing

Ferroelectric Field Effect Transistors (FeFETs): Advancements, challenges and exciting prospects for next generation Non-Volatile Memory (NVM) applications

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Effect of the Si Doping Content in HfO₂ Film on the Key Performance Metrics of Ferroelectric FETs