Investigating Ferroelectric Minor Loop Dynamics and History Effect—Part I: Device Characterization

Wang, Panni; Wang, Zheng; Sun, Xiaoyu; Hur, Jae; Datta, Suman; Khan, Asif Islam; Yu, Shimeng

doi:10.1109/ted.2020.3009623

Cited by 23 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike conventional Fe-FET that only has two states, the use of analog switching in ferroelectric material generates a range of intermediate states, which is beneficial for developing energy-efficient, high-density memory and synapse using FeFETs (Figure 1a). [27][28][29][30] The performance of FeFETs is significantly affected by the properties of the ferroelectric material, such as leakage current, interface trap density, memory window, C2C variation, retention, and endurance. [16][17][18][19][20] Therefore, it is crucial to establish a comprehensive understanding of the ferroelectric material used in the gate stack of the Fe-FET.…”

Section: Ferroelectric Switching Propertiesmentioning

confidence: 99%

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two‐Dimensional Materials Integration

Xiang

Chien

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

In‐memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time‐consuming challenges associated with the von Neumann architecture. The ferroelectric field‐effect transistor (FeFET) technology, with its fast and energy‐efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study, the capabilities of an integrated ferroelectric HfO2 and 2D MoS2 channel FeFET in achieving high‐performance 4‐bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET‐based in‐memory computing for future neuromorphic computing applications.

show abstract

Section: Ferroelectric Switching Propertiesmentioning

confidence: 99%

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two‐Dimensional Materials Integration

Xiang

Chien

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

show abstract

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

confidence: 99%

“…In addition, because the partially polarized states depend on the previous polarization state, the pulse induced to achieve targeted polarization states should be optimized while considering the history of polarization. [83][84][85] This is demonstrated using phase-field modeling (PFM) based on the time-dependent Landau-Ginzburg equation. Therefore, because variations in the Landau coefficient are caused by phase stability, the description of the history effect in domain dynamics depends on the polar/nonpolar phase stability.…”

Section: Advantages and Challenges Of Neuromorphic Computing Devicesmentioning

confidence: 99%

Neuromorphic devices based on fluorite‐structured ferroelectrics

Lee

Park

Kim

et al. 2022

InfoMat

View full text Add to dashboard Cite

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.

show abstract

“…It consists of an additional ferroelectric layer of dopedhafnium oxide (HfZrO x ) in the gate stack. The polarizationstate of the ferroelectric layer dictates the threshold voltage and may be tuned with the help of electrical pulses applied at the gate and drain terminals [23], [26], [27]. HfZrO x -based ferroelectric FETs with a capability of tuning the weights (conductance states) with more than 5-bit precision have already been experimentally demonstrated [25], [27].…”

Section: Fe-finfet Structure and Modeling Approachmentioning

confidence: 99%

“…HfZrO x -based ferroelectric FETs with a capability of tuning the weights (conductance states) with more than 5-bit precision have already been experimentally demonstrated [25], [27]. In this work, we have utilized an experimentally calibrated compact model for Fe-FinFETs [28] which accurately captures the static polarization characteristics, program/erase behavior, temporal dynamics during transient analysis and the history effect of a HfZrO x stack [26]. The parameters of the compact model have been tuned to reproduce the experimental characteristics of the HfZrO x -based ferroelectric capacitor reported in [25] and shown in Fig.…”

Section: Fe-finfet Structure and Modeling Approachmentioning

confidence: 99%

Ultra-Compact Neural Network ADC Exploiting Ferroelectric FET

Banerjee¹,

Bhattacharya²,

Chauhan³

et al. 2022

Preprint

View full text Add to dashboard Cite

<div>Development of ultra-compact, low-to-medium precision analog-to-digital converters (ADCs) with unprecedented energy-efficiency is essential to meet the ever-increasing demand for data converters in advanced computing systems including neuromorphic accelerators based on emerging non-volatile memories. To this end, in this work, for the first time, we propose a feedforward neural network ADC based on a network of highly scalable, CMOS-compatible, and energy-efficient ferroelectric-FinFET (Fe-FinFET) synaptic elements. Our lower triangular neural network (LTNN) ADC design, implemented using 7-nm technology from ARM along with an experimentally calibrated compact model for Fe-FinFETs, consumes 5.44 μW of power, 1.03 μm<sup>2</sup> of area while operating at a speed of 1.23 megasamples per second for 4-bit precision. The proposed neural network ADC may pave the way for realization of highly efficient neuromorphic processing engines and neuro-optimizers based on cross-point array of emerging non-volatile memories.</div>

show abstract

Investigating Ferroelectric Minor Loop Dynamics and History Effect—Part I: Device Characterization

Cited by 23 publications

References 24 publications

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two‐Dimensional Materials Integration

Enhancing Memory Window Efficiency of Ferroelectric Transistor for Neuromorphic Computing via Two‐Dimensional Materials Integration

Neuromorphic devices based on fluorite‐structured ferroelectrics

Ultra-Compact Neural Network ADC Exploiting Ferroelectric FET

Contact Info

Product

Resources

About