华中科技大学学报（自然科学版）2026，Vol.54Issue(3)：16-21,6.DOI:10.13245/j.hust.250184
迁移学习在3D NAND闪存温度敏感可靠性预测中的应用
Application of transfer learning in temperature-sensitive reliability prediction for 3D NAND flash memory
摘要
Abstract
A temperature-adaptive reliability prediction model was proposed in this study.Based on transfer learning,relatively easy-to-obtain normal-temperature or constant-temperature test data were utilized by the model to construct a preliminary reliability prediction model,with the core characteristics and failure patterns of flash memory reliability captured.On this basis,through transfer learning techniques,migration operations were performed using a small amount of data collected under changing temperature conditions to enhance the model's generalization ability in different temperature environments and strengthen its adaptability to variable-temperature environments.The variable-temperature prediction error is reduced from 6.16× 10-5 to 3.49× 10-5,with a 43.3％improvement in prediction accuracy.It is demonstrated by experiments that stable prediction performance is maintained by the model within a wide temperature range from-40℃ to 85℃,and good adaptability to temperature fluctuations is exhibited by the model.While ensuring the same prediction accuracy,the training overhead required by transfer learning is significantly reduced.关键词
3D NAND闪存/可靠性/温度变化/迁移学习/长短期记忆网络Key words
3D NAND Flash/reliability/temperature changes/transfer learning/long short-term memory networks分类
信息技术与安全科学引用本文复制引用
刘政林,骆一凡,潘玉茜,张浩明..迁移学习在3D NAND闪存温度敏感可靠性预测中的应用[J].华中科技大学学报（自然科学版）,2026,54(3):16-21,6.基金项目
国家自然科学基金资助项目(62274068). （62274068）
