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

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 (V_th) window, which is crucial for NAND operation. The energetic-trap distribution is a critical material property of the CTN that affects the V_th window between the erase and program V_th. An artificial neural network (ANN) was used to model the relationship between the energetic-trap distributions as an input parameter and the V_th 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 (N_TD and N_TA) and their standard deviations (σ_TD and σ_TA) were found to most strongly impact the V_th window. As they increased, the V_th window increased because of the availability of a larger number of trap sites. Finally, when the ML-optimized energetic-trap distributions were simulated, the V_th 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.