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.