skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Depolarization Field Induced Instability of Polarization States in HfO 2 Based Ferroelectric FET
Doped HfO2 based ferroelectric FET (FeFET) exhibits a greatly improved retention performance compared with its perovskite counterpart due to its large coercive field, which prevents domain flip during retention. In this work, however, through extensive temperature dependent experimental characterization and modeling, we are demonstrating that: 1) with FeFET geometry scaling, the polarization states are no longer stable, but exhibit multi-step degradation and cause reduced sense margin in distinguishable adjacent levels or even eventual memory window collapse; 2) the instability is caused by the temperature activated accumulation of switching probability under depolarization field stress, which could cause domain switching within the retention time at operating temperatures.  more » « less
Award ID(s):
1810005
PAR ID:
10243509
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
020 IEEE International Electron Devices Meeting (IEDM)
Page Range / eLocation ID:
4.5.1 to 4.5.4
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, IEEE electron device letters)
    Ha, D. (Ed.)
    An optimization principle for ferroelectric FET (FeFET), centered around charge matching between the ferroelectric and its underlying semiconductor, is theoretically investigated. This letter shows that, by properly reducing the ferroelectric polarization charge and its background dielectric constant, charge matching can be improved to enable simultaneously: i) reduction of the interlayer and semiconductor electric fields during programming, reading, and retention, leading to prolonged endurance and retention; ii) improvement of the memory window; and iii) suppression of device-to-device variations by affording full polarization switching. These attributes provide an incentive for the presentation of the proposed guidelines for FeFET optimization as detailed in this letter. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al (, Science Advances)
    Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)–based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively. 
    more » « less
  3. ; ; ; ; ; ; ; ; (, Advanced Science)
    Abstract Ferroelectrics, due to their polar nature and reversible switching, can be used to dynamically control surface chemistry for catalysis, chemical switching, and other applications such as water splitting. However, this is a complex phenomenon where ferroelectric domain orientation and switching are intimately linked to surface charges. In this work, the temperature‐induced domain behavior of ferroelectric‐ferroelastic domains in free‐standing BaTiO3films under different gas environments, including vacuum and oxygen‐rich, is studied by in situ scanning transmission electron microscopy (STEM). An automated pathway to statistically disentangle and detect domain structure transformations using deep autoencoders, providing a pathway towards real‐time analysis is also established. These results show a clear difference in the temperature at which phase transition occurs and the domain behavior between various environments, with a peculiar domain reconfiguration at low temperatures, from a‐c to a‐a at ≈60 °C. The vacuum environment exhibits a rich domain structure, while under the oxidizing environment, the domain structure is largely suppressed. The direct visualization provided by in situ gas and heating STEM allows to investigate the influence of external variables such as gas, pressure, and temperature, on oxide surfaces in a dynamic manner, providing invaluable insights into the intricate surface‐screening mechanisms in ferroelectrics. 
    more » « less
  4. ; ; ; ; ; ; ; ; ; (, 2018 IEEE International Electron Devices Meeting (IEDM))
    We report the first experimental demonstration of ferroelectric field-effect transistor (FEFET) based spiking neurons. A unique feature of the ferroelectric (FE) neuron demonstrated herein is the availability of both excitatory and inhibitory input connections in the compact 1T-1FEFET structure, which is also reported for the first time for any neuron implementations. Such dual neuron functionality is a key requirement for bio-mimetic neural networks and represents a breakthrough for implementation of the third generation spiking neural networks (SNNs)-also reported herein for unsupervised learning and clustering on real world data for the first time. The key to our demonstration is the careful design of two important device level features: (1) abrupt hysteretic transitions of the FEFET with no stable states therein, and (2) the dynamic tunability of the FEFET hysteresis by bias conditions which allows for the inhibition functionality. Experimentally calibrated, multi-domain Preisach based FEFET models were used to accurately simulate the FE neurons and project their performance at scaled nodes. We also implement an SNN for unsupervised clustering and benchmark the network performance across analog CMOS and emerging technologies and observe (1) unification of excitatory and inhibitory neural connections, (2) STDP based learning, (3) lowest reported power (3.6nW) during classification, and (4) a classification accuracy of 93%. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Science Advances)
    Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼106×/10× and ∼106×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email