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Synergistic Approach of Interfacial Layer Engineering and READ-Voltage Optimization in HfO2-Based FeFETs for

Yannick Raffel1, Sourav De1, Maximilian Lederer1

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Summary
This summary is machine-generated.

Hafnium oxide ferroelectric field-effect transistors (FeFETs) show improved performance with interfacial layer engineering. SiON-based FeFETs achieve 96% accuracy in MNIST handwritten digit recognition for inference tasks.

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Ferroelectric field-effect transistors (FeFETs) are promising for neuromorphic computing.
  • Hafnium oxide (HfO2)-based FeFETs offer potential for high-performance synaptic devices.
  • Interfacial layer (IL) engineering is crucial for optimizing FeFET characteristics.

Purpose of the Study:

  • To enhance the performance of HfO2-based FeFETs through interfacial layer engineering and READ-voltage optimization.
  • To evaluate the suitability of FeFETs with different interfacial layers (SiO2 and SiON) as synaptic devices for neuromorphic applications.
  • To assess the impact of device degradation on neural network performance for inference-only operations.

Main Methods:

  • Fabrication and characterization of FeFET devices with SiO2 and SiON interfacial layers.
  • Neuromorphic simulations to evaluate FeFETs as synaptic devices.
  • System-level simulations to analyze degradation effects (retention and noise) on neural network accuracy.

Main Results:

  • FeFETs with SiO2 IL showed better low-frequency characteristics, while SiON IL exhibited superior WRITE endurance and retention.
  • Device WRITE endurance was insufficient for online neural network training, necessitating an inference-only approach.
  • System-level simulations indicated retention degradation significantly impacts inference-only operations more than low-frequency noise.
  • A neural network utilizing SiON IL-based FeFETs achieved 96% accuracy on the MNIST dataset for inference, with a minor 2.5% deviation from the software baseline.

Conclusions:

  • Synergistic engineering of interfacial layers and READ-voltage optimization can improve FeFET performance.
  • SiON-based FeFETs are suitable for inference-only neuromorphic applications despite retention degradation challenges.
  • Offline training followed by inference-only operation is a viable strategy for FeFET-based neuromorphic systems.
  • Further improvements in WRITE endurance and retention are necessary for more demanding neuromorphic tasks.