Impact of Random Spatial Fluctuation in Non-Uniform Crystalline Phases on the Device Variation of Ferroelectric FET

Garg, Chirag; Chauhan, Nitanshu; Deng, Shan; Khan, Asif Islam; Dasgupta, S.; Bulusu, Anand; Ni, Kai

doi:10.1109/led.2021.3087335

Cited by 37 publications

(14 citation statements)

References 20 publications

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“…The discovery of ferroelectricity in the hafnium oxide material system enabled a large variety of device applications, ranging from nonvolatile memories , and neuromorphic devices − to pyroelectric sensors and piezoelectric actuators. − However, recent studies have highlighted the importance of the grain composition on the device performance, − e.g., due to the presence of percolation paths in ferroelectric field-effect transistors . Therefore, microstructure engineering is of major importance to further improve the device performance.…”

Section: Introductionmentioning

confidence: 99%

Influence of Annealing Temperature on the Structural and Electrical Properties of Si-Doped Ferroelectric Hafnium Oxide

Lederer

Bagul

Lehninger

et al. 2021

ACS Appl. Electron. Mater.

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The ferroelectric properties of hafnium oxide films are strongly influenced by the crystallization process due to the interaction of thermodynamics, kinetics, and mechanical stress. In this work, the influence of annealing temperature on the crystallographic properties and microstructure of Si-doped hafnium oxide thin films as well as their ferroelectric properties are investigated by X-ray diffraction, transmission Kikuchi diffraction, and electrical characterization. The findings reveal the emergence of a [100] and [110] out-of-plane texture for metal−ferroelectric− metal (MFM) and metal−ferroelectric−insulator−semiconductor (MFIS) capacitor structures with increasing annealing temperature, respectively. In combination with observed stress relaxation at higher temperatures and the evolution of the wake-up behavior, insights into the crystallization process and the influence of the interplay of microstructure and stress on the ferroelectric properties of hafnium oxide thin films are given.

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Section: Introductionmentioning

confidence: 99%

Influence of Annealing Temperature on the Structural and Electrical Properties of Si-Doped Ferroelectric Hafnium Oxide

Lederer

Bagul

Lehninger

et al. 2021

ACS Appl. Electron. Mater.

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“…It has been furthermore shown that changes in the crystallographic texture and grain size strongly alter the switching behavior [67,68]. Moreover, an impact on device variability due to grain statistics has been confirmed as well [58][59][60]69].…”

Section: Microstructure-based Variability In Ferroelectric Responsementioning

confidence: 96%

“…Another source of device variability is the microstructure of the film [57][58][59][60][61]. This includes the grain structure, local crystallographic phase and local crystallographic orientation.The introduction of transmission Kikuchi diffraction as a viable method to study hafnium oxide enabled a detailed study of these parameters [62].…”

Section: Microstructure-based Variability In Ferroelectric Responsementioning

confidence: 99%

Monolithic-3D Inference Engine with IGZO Based Ferroelectric Thin Film Transistor Synapses

De¹,

Lederer²,

Raffel³

et al. 2023

Preprint

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<p> Instigated by the plethora of data generated by edge devices and IoT devices, machine learning has become the de facto choice of everyone for solving many tasks. Applications such as intelligent healthcare monitoring systems, smart watches, or automatic cars require real-time processing of the data or image, which is done by machine learning algorithms with higher efficiency than humans. There are two possible methods for artificial intelligence?1. non-von-Neumann hardware-based implementation of neural networks 2. traditional computer science based approach for neural networks or traditional von-Neumann architecture?based implementation of neural networks The standard von-Neumann performance of neural networks, where the memory and computation parts are segregated, suffers from severe latency with increasing number of edge devices. However, the multitude of edge devices used in our daily life imposes strict restrictions on latency, device area, and power consumption for hardware. Therefore, we need to take the route beyond the CMOS-based mixed-signal implementation of neural networks, where the memory bandwidth is not limited by the quintessential von-Neumann archi?tecture. The primary motivation of present-day research on the non-von-Neumann computing architecture is to build dedicated hardware modules for implementing low-power, fast-computing units without affecting the recent trend of scaling. This chapter focuses on amorphous indium-gallium-zinc-oxide (α-IGZO) based and their system-level applications. Back-end-of-line (BEoL) compatible indium IGZO based multibit one-time programmable (OTP) ferroelectric thin film transistors (FeTFT) with lifelong retention capability were fabricated. The maximum temperature of the entire fabrication process was limited to 350oC. The gate-stack engineering by varying the thickness ratio of ferroelectric hafnium zirconium oxide (HZO) and IGZO layer fomented excellent data-retention capability and one time programming property. Further, we have evaluated the performance of IGZO-based FeTFT as synaptic devices for an inference engine. The system-level simulation revealed inference accuracy loss of only 1.5% after ten years without re-training for Modified National Institute of Standards and Technology (MNIST) hand-written digits in a multi-layer perceptron (MLP) neural network with a baseline of 97%. The proposed inference engine also showed superior energy efficiency and cell area of 95.33 TOPS/W (binary) and 8F2 , respectively. </p>

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“…due to the formation of current percolation paths. [182,183] Finiteelement simulations indicate that monoclinic grains can act as leakage paths. [183,184] Moreover, it has been demonstrated that the grain size plays an important role on the switching behavior.…”

Section: Neuromorphic Devicesmentioning

confidence: 99%

“…[182,183] Finiteelement simulations indicate that monoclinic grains can act as leakage paths. [183,184] Moreover, it has been demonstrated that the grain size plays an important role on the switching behavior. [185] Depending on the device layout, analogue or digital switching can be achieved by either using a wide and short or a long and narrow device, respectively.…”

Section: Neuromorphic Devicesmentioning

confidence: 99%

Review on the Microstructure of Ferroelectric Hafnium Oxides

Lederer

Lehninger

Ali

et al. 2022

Physica Rapid Research Ltrs

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Ferroelectric hafnium oxide is of major interest for a multitude of applications in microelectronics, ranging from neuromorphic devices to actuators and sensors. While the electrical performance is commonly discussed in depth, the influence of the microstructure is often disregarded. However, in recent years, more research groups shed light into the microstructural background of ferroelectric behavior in hafnium oxide films. To give a more general and complete picture of the different influences on the microstructure and its relevance for the applications, the process and stack influences on the microstructure are reviewed and summarized. While a few mechanisms are not yet understood in depth, a coherent picture on the formation of the microstructure in ferroelectric hafnium oxide layers can be gained.

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Impact of Random Spatial Fluctuation in Non-Uniform Crystalline Phases on the Device Variation of Ferroelectric FET

Cited by 37 publications

References 20 publications

Influence of Annealing Temperature on the Structural and Electrical Properties of Si-Doped Ferroelectric Hafnium Oxide

Influence of Annealing Temperature on the Structural and Electrical Properties of Si-Doped Ferroelectric Hafnium Oxide

Monolithic-3D Inference Engine with IGZO Based Ferroelectric Thin Film Transistor Synapses

Review on the Microstructure of Ferroelectric Hafnium Oxides

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