
Krystal S. 2025 | BASIS Independent Fremont
- Project Title: Using Machine Learning to Predict Sensing Voltage Changes in NAND Flash Cells to Maximize Data Retention
- BASIS Independent Advisor: Mr. Dievendorf
With technology’ increasing influence on society, maintaining data reliability is crucial, especially for healthcare centers, where medical information safekeeping directly impact patients. Currently, data degradation shortens storage devices’ lifespans and makes them unpredictable, which leads to prevalent data loss. The goal of my project is to maximize the duration of accurate data. One solution lies in knowing the sensing voltage—an accurate sensing voltage prediction indicates a strong likelihood that the data will be maintained precisely and estimates the storage’s current stage in its total lifespan. Drawing from my experience in coding a storage lifespan elongation program in 2023 and 2024, I plan to code a similar program that predicts the sensing voltage of each storage disk. To achieve this, I will first collect initial sensing voltage data under controlled environmental conditions. Following, I plan to program a machine learning model (MLM) that predicts future sensing voltage data, and finally, I will validate the model's predictions against observed outcomes. I anticipate that the MLM will maintain data accuracy by 15-30% longer. My project contributes to a more sustainable NAND flash storage design with improved lifespan and performance. This change may have significant impacts on sectors like healthcare, where preserving data integrity can save lives.