Neural Network-Based prediction for Cross-Temperature induced VT distribution shift in 3D NAND flash memory

In this study, a neural network (NN) was proposed for predicting the V_T characteristics of NAND flash memories under cross-temperature conditions. The training data were obtained from commercial NAND flash memory chip measurements at various temperatures. The V_T distribution shift caused by cross-temperature was accurately predicted by investigating the optimum data dimensions while minimizing the data generation process. Two types of NNs were used to achieve an accurate V_T distribution prediction, and each network was optimized using specific parameters based on the data characteristics at various program verify levels. Finally, quantitative and visual evaluations were conducted to verify the performance of the trained NNs. When the program-measured temperature varied from low to high, the NNs achieved mean errors of 1.87%, 1.41% at low and 0.34%, 0.77% at high for the average and width of the V_T distribution, respectively. Similarly, when the temperature varied from high to low, the corresponding mean errors were 2.01%, 0.74% at high and 0.23%, 1.59% at low. These findings demonstrate that NNs can minimize the procedures for detecting the V_T distribution shift caused by cross-temperature, thereby offering a promising approach to enhance reliability in the presence of such effects.