Yi Fang

Title: Yi Fang: Innovator in Deep Learning for NAND Flash Memory

Introduction

Yi Fang is a prominent inventor based in Fujian, China. He has made significant contributions to the field of deep learning, particularly in optimizing quantization methods for NAND flash memory. His innovative approach has the potential to enhance the reliability of memory storage systems.

Latest Patents

Yi Fang holds a patent titled "Method for optimizing polar-RNNA quantizer of MLC-type NAND flash memory on basis of deep learning." This method involves transforming an MLC flash detection task into a deep learning task. It includes obtaining three hard-decision read thresholds based on a neural network, expanding six soft-decision read thresholds, constructing an LLR mapping table, and optimizing the soft-decision read thresholds using a genetic algorithm. This invention allows polar codes to be directly utilized for MLC flash channels, significantly improving their reliability.

Career Highlights

Yi Fang is affiliated with Fuzhou University, where he continues to advance research in the field of deep learning and memory technology. His work has garnered attention for its practical applications in enhancing the performance of NAND flash memory systems.

Collaborations

He collaborates with notable colleagues, including Pingping Chen and Zhen Mei, who contribute to his research endeavors.

Conclusion

Yi Fang's innovative work in optimizing NAND flash memory through deep learning techniques showcases his commitment to advancing technology in this critical area. His contributions are paving the way for more reliable memory storage solutions.