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Related Concept Videos

Biasing of FET01:22

Biasing of FET

375
Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
375

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Optimal Energetic-Trap Distribution of Nano-Scaled Charge Trap Nitride for Wider V Window in 3D NAND Flash Using a

Kihoon Nam1, Chanyang Park1, Jun-Sik Yoon1

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Summary

Machine learning optimized charge trap nitride in 3D NAND Flash. This technique widened the threshold voltage window by 49%, improving NAND operation and enabling optimized cell transistor processes.

Keywords:
3D NAND Flashcharge trap nitridegradient-descent methodmachine learningmulti-level cellthreshold voltage windowtrap distribution

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • The threshold voltage (V) window is critical for 3D NAND Flash operation.
  • Energetic-trap distribution in charge trap nitride (CTN) significantly affects the V window.
  • Optimizing CTN material properties is key to enhancing NAND performance.

Purpose of the Study:

  • To employ a machine learning (ML) technique for optimizing energetic-trap distributions in CTN for 3D NAND Flash.
  • To widen the threshold voltage (V) window by fine-tuning trap properties.
  • To establish a method for predicting and maximizing the V window based on trap characteristics.

Main Methods:

  • Utilized an artificial neural network (ANN) to model the relationship between energetic-trap distributions and the V window.
  • Employed the gradient-descent method with a trained ANN to identify optimal input parameters.
  • Simulated ML-optimized energetic-trap distributions to validate performance improvements.

Main Results:

  • Identified trap densities (N and N) and their standard deviations (σ and σ) as key factors influencing the V window.
  • Demonstrated that increased trap densities and standard deviations lead to a wider V window.
  • Achieved a 49% increase in the V window through ML-optimized CTN properties compared to experimental values.

Conclusions:

  • The developed ML technique effectively optimizes CTN material properties for 3D NAND Flash.
  • This approach can be applied to enhance cell transistor processes by precisely controlling CTN characteristics.
  • ML-driven optimization offers a pathway to significantly improve the performance and reliability of 3D NAND Flash memory.