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Time-Dependent Current Transport Model for Ferroelectric Tunnel Junctions.

Tie-Lin Kong1,2, Jie Bie1,2,3, Zhuo Chen1,2

  • 1National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing, Jiangsu 210023, China.

ACS Applied Materials & Interfaces
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Summary

A new model simulates ferroelectric tunnel junction (FTJ) behavior for artificial intelligence (AI) computing. This method accurately predicts synaptic functions, accelerating AI hardware development.

Keywords:
current/electron transport modelferroelectric memristorsferroelectric tunnel junctionsnonequilibrium Green’s functionsynaptic functions

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computer Engineering

Background:

  • Memristors offer nonvolatile resistance states, crucial for overcoming the von Neumann bottleneck in AI computing.
  • Ferroelectric memristors, particularly ferroelectric tunnel junctions (FTJs), are promising for neuromorphic computing due to their polarization switching mechanism.
  • Understanding the current transport dynamics in FTJs is essential for optimizing their performance in AI applications.

Purpose of the Study:

  • To develop a time-dependent current transport model for ferroelectric tunnel junctions (FTJs).
  • To accurately simulate the synaptic functions of FTJs for neuromorphic computing without experimental procedures.
  • To accelerate the research and development of advanced AI hardware.

Main Methods:

  • Integration of Thomas-Fermi screening theory, nonequilibrium Green's Function (NEGF), and polarization reversal dynamics.
  • Development of a multidomain polarization switching model to estimate polarization state proportions.
  • Utilizing CuInP2S6 (CIPS) as a model 2D van der Waals ferroelectric material for FTJ construction and testing.

Main Results:

  • A novel time-dependent current transport model for FTJs was successfully developed and validated.
  • The model accurately estimates current-voltage curves and synaptic functions of FTJs.
  • The coercive field and polarization reversal speed of thin films can be estimated using the proposed model.

Conclusions:

  • The developed model offers an efficient and accurate approach for simulating FTJ behavior.
  • This computational tool accelerates the design and optimization of FTJs for neuromorphic computing and AI applications.
  • The study provides a pathway for rapid advancement in memristor-based AI hardware.