Ferroelectric FET based Non-Volatile Analog Synaptic Weight Cell

Abstract

Dense analog synaptic crossbar arrays are a promising candidate for neuromorphic hardware accelerators due to the ability to mitigate data movement by performing in-situ vector-matrix products and weight updates within the storage array itself. However, many analog weight storage cells suffer from long latencies or low dynamic ranges, limiting the achievable performance. In this work, we demonstrate that the voltage-controlled partial polarization switching dynamics in ferroelectric-field-effect transistors (FeFET) can be harnessed to enable a 32 state non-volatile analog synaptic weight cell with large dynamic range (67) and low latency weight updates (50 ns) for an amplitude modulated pulse scheme.

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Document Details

Document Type
Technical Report
Publication Date
Mar 25, 2019
Accession Number
AD1075675

Entities

People

  • Arman Kazemi
  • Jianchi Zhang
  • Kai Ni
  • Matthew Jerry
  • Michael Niemier
  • Pai-yu Chen
  • Pankaj Sharma
  • Shimeng Yu
  • Sourav Dutta
  • Suman Datta
  • X. S. Hu

Organizations

  • University of Notre Dame

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Cell Size
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Dynamic Range
  • Electric Fields
  • Energy Efficiency
  • Engineering
  • Field Effect Transistors
  • Image Recognition
  • Machine Learning
  • Metal Oxide Semiconductors
  • Neural Networks
  • Semiconductors
  • Training
  • Transistors

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Distributed Systems and Data Platform Development