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

• Neural networks (NNs) were proposed for predicting V T of NAND flash memories at cross-temperature. • Two different types of NNs were used for accurate prediction. • Quantitative and visual evaluations performed to verify the performance of the trained NNs. • Application of NNs can contribute to improving the reliability of NAND flash memory by capturing the V T distribution at cross-temperature. 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. [ABSTRACT FROM AUTHOR]