Nanomaterials (Nanomaterials)

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

  • Kihoon Nam (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Chanyang Park (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Jun-Sik Yoon (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Hyeok Yun (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Hyundong Jang (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Kyeongrae Cho (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH)) ;
  • Ho-Jung Kang (SK hynix Inc.) ;
  • Min-Sang Park (SK hynix Inc.) ;
  • Jaesung Sim (SK hynix Inc.) ;
  • Hyun-Chul Choi (Department of Electrical Engineering, Yeungnam University) ;
  • Rock-Hyun Baek (Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH))
  • Received : 2022.03.28
  • Accepted : 2022.05.23
  • Published : 20220000
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Abstract

A machine-learning (ML) technique was used to optimize the energetic-trap distributions of nano-scaled charge trap nitride (CTN) in 3D NAND Flash to widen the threshold voltage (Vth) window, which is crucial for NAND operation. The energetic-trap distribution is a critical material property of the CTN that affects the Vth window between the erase and program Vth. An artificial neural network (ANN) was used to model the relationship between the energetic-trap distributions as an input parameter and the Vth window as an output parameter. A well-trained ANN was used with the gradient-descent method to determine the specific inputs that maximize the outputs. The trap densities (NTD and NTA) and their standard deviations (σTD and σTA) were found to most strongly impact the Vth window. As they increased, the Vth window increased because of the availability of a larger number of trap sites. Finally, when the ML-optimized energetic-trap distributions were simulated, the Vth window increased by 49% compared with the experimental value under the same bias condition. Therefore, the developed ML technique can be applied to optimize cell transistor processes by determining the material properties of the CTN in 3D NAND Flash.

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Acknowledgement

The EDA tool was supported by the IC Design Education Center (IDEC).