TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Bailon, Daniel Nicolas
A1  - Taburet, Guillaume
A1  - Shavgulidze, Sergo
A1  - Freudenberger, Jürgen
T1  - Neural network aided reference voltage adaptation for NAND flash memory
T2  - 12th IEEE International Conference on Consumer Electronics (ICCE-Berlin 2022), 2-6 Sept. 2022, Berlin, Germany
N2  - Large persistent memory is crucial for many applications in embedded systems and automotive computing like AI databases, ADAS, and cutting-edge infotainment systems. Such applications require reliable NAND flash memories made for harsh automotive conditions. However, due to high memory densities and production tolerances, the error probability of NAND flash memories has risen. As the number of program/erase cycles and the data retention times increase, non-volatile NAND flash memories' performance and dependability suffer. The read reference voltages of the flash cells vary due to these aging processes. In this work, we consider the issue of reference voltage adaption. The considered estimation procedure uses shallow neural networks to estimate the read reference voltages for different life-cycle conditions with the help of histogram measurements. We demonstrate that the training data for the neural networks can be enhanced by using shifted histograms, i.e., a training of the neural networks is possible based on a few measurements of some extreme points used as training data. The trained neural networks generalize well for other life-cycle conditions.
Y1  - 2022
SN  - 978-1-6654-5676-0
SB  - 978-1-6654-5676-0
SN  - 978-1-6654-5677-7
SB  - 978-1-6654-5677-7
U6  - https://doi.org/10.1109/ICCE-Berlin56473.2022.9937118
DO  - https://doi.org/10.1109/ICCE-Berlin56473.2022.9937118
N1  - Volltext im Campusnetz der Hochschule Konstanz via Datenbank IEEE Xplore abrufbar.
SP  - 5
S1  - 5
PB  - IEEE
ER  -