Superlattice HfO2-ZrO2 based Ferro-Stack HfZrO2 FeFETs: Homogeneous-Domain Merits Ultra-Low Error, Low Programming Voltage 4 V and Robust Endurance 109 cycles for Multibit NVM

Liao, C.-Y.; Lou, Z.-F.; Lin, Cheng-Hung; Senapati, A.; Karmakar, R.; Li, Z.-X.; Ray, W.-C.; Lee, Jia-Yang; Chen, Ping Hung; Hsiang, K.-Y.; Tseng, Hsin-Chun; Wang, C.-C.; Tsai, Jeng-Han; Tang, Y.-T.; Chang, Shuo‐Hung; Liu, C. W.; Maikap, S.; Lee, M. H.

doi:10.1109/iedm45625.2022.10019369

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…The p-FeFET shows exceptional endurance characteristics over 10 10 cycles. It should be noted that these values are superior to the previously reported characteristics of FeFET. − As mentioned above, excellent endurance characteristics were achieved with the MFMIS structure. On the other hand, the p-FeFET shows relatively poor retention characteristics compared to n-FeTFT.…”

Section: Resultsmentioning

confidence: 99%

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Hwang,

Joh,

Kim

et al. 2024

ACS Appl. Mater. Interfaces

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Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F 2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.

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Section: Resultsmentioning

confidence: 99%

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Hwang,

Joh,

Kim

et al. 2024

ACS Appl. Mater. Interfaces

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Perspective Roadmap of Advanced HfO₂-based Ferroelectric Field Effect Transistors

De,

Cho,

Ali

et al. 2024

2024 8th IEEE Electron Devices Technology &Amp;amp; Manufacturing Conference (EDTM)

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Low Polarization Loss of Long Endurance on Scavenged Ru-Based Electrode Ferroelectric Hf_0.5Zr_0.5O₂ by Optimizing TiN_x Interfacial Capping Layer and Its Fatigue Mechanism

Senapati,

Lou,

Lee

et al. 2024

IEEE Electron Device Lett.

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Superlattice HfO₂-ZrO₂ based Ferro-Stack HfZrO₂ FeFETs: Homogeneous-Domain Merits Ultra-Low Error, Low Programming Voltage 4 V and Robust Endurance 10⁹ cycles for Multibit NVM

Cited by 6 publications

References 9 publications

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Perspective Roadmap of Advanced HfO₂-based Ferroelectric Field Effect Transistors

Low Polarization Loss of Long Endurance on Scavenged Ru-Based Electrode Ferroelectric Hf_0.5Zr_0.5O₂ by Optimizing TiN_x Interfacial Capping Layer and Its Fatigue Mechanism

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Superlattice HfO2-ZrO2 based Ferro-Stack HfZrO2 FeFETs: Homogeneous-Domain Merits Ultra-Low Error, Low Programming Voltage 4 V and Robust Endurance 109 cycles for Multibit NVM

Cited by 6 publications

References 9 publications

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Perspective Roadmap of Advanced HfO2-based Ferroelectric Field Effect Transistors

Low Polarization Loss of Long Endurance on Scavenged Ru-Based Electrode Ferroelectric Hf0.5Zr0.5O2 by Optimizing TiN x Interfacial Capping Layer and Its Fatigue Mechanism

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Product

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About

Superlattice HfO₂-ZrO₂ based Ferro-Stack HfZrO₂ FeFETs: Homogeneous-Domain Merits Ultra-Low Error, Low Programming Voltage 4 V and Robust Endurance 10⁹ cycles for Multibit NVM

Perspective Roadmap of Advanced HfO₂-based Ferroelectric Field Effect Transistors

Low Polarization Loss of Long Endurance on Scavenged Ru-Based Electrode Ferroelectric Hf_0.5Zr_0.5O₂ by Optimizing TiN_x Interfacial Capping Layer and Its Fatigue Mechanism