Variability Analysis for Ferroelectric Field-Effect Transistors

Choe, Gihun; Yu, Shimeng

doi:10.1109/edtm50988.2021.9420980

Cited by 16 publications

(9 citation statements)

References 6 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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“…7 shows the influences of WFV and PV on the 28 nm and 22 nm FeFETs. The gate LER is not considered because the deviation by the LER is smaller than the others [12]. The Vth has been extracted using the constant current method at 1 μA/μm.…”

Section: Resultsmentioning

confidence: 99%

“…Next, the impacts of PV, WFV, and LER on the threshold voltage have been compared depending on the different technology nodes (bulk at 28 nm, FDSOI at 22 nm, FinFET at 14 nm and 7 nm). This article is an extension of our conference paper [12], where only 28 nm analysis was reported.…”

Section: Variability Study Of Ferroelectric Field-effectmentioning

confidence: 88%

“…1(d) shows the conceptual image of WFV, LER and PV included in this simulation. Specifically, since the gate LER does not affect the device performance significantly for the bulk FeFET compared to the WFV and PV [12], it is not included for bulk FeFET simulation. On the other hand, the fin LER has been adopted as well as the WFV and PV for the FeFETs with the FinFET structure (Fe-FinFETs).…”

Section: Simulation Methodsmentioning

confidence: 99%

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Variability Study of Ferroelectric Field-Effect Transistors Towards 7nm Technology Node

Choe

2021

IEEE J. Electron Devices Soc.

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The random variation sources have a significant influence on the performance of ferroelectric field-effect transistor (FeFET). In this work, comparative analysis on the process variation induced variability of FeFET towards a 7 nm technology node has been conducted, including different device structures from bulk to FDSOI and FinFET. The random ferroelectric/dielectric phase variation (PV), the metal work function variation (WFV) and the line-edge roughness (LER) effects are incorporated in TCAD simulations to quantitatively investigate their impacts on the threshold voltage variation. Especially, the Voronoi diagram is employed to realistically model the ferroelectric grain distributions and to accurately simulate the impact of PV on FeFET characteristics.

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“…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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Variability Analysis for Ferroelectric Field-Effect Transistors

Cited by 16 publications

References 6 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

Variability Study of Ferroelectric Field-Effect Transistors Towards 7nm Technology Node

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

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