Riqing Chen

Title: Riqing Chen: Innovator in NAND Flash Memory Optimization

Introduction

Riqing Chen is a prominent inventor based in Fujian, China. He has made significant contributions to the field of NAND flash memory optimization through his innovative methods. His work focuses on enhancing the reliability of MLC (Multi-Level Cell) flash memory using advanced deep learning techniques.

Latest Patents

Riqing Chen holds a patent titled "Method for optimizing polar-RNNA quantizer of MLC-type NAND flash memory on basis of deep learning." This patent outlines a method that transforms an MLC flash detection task into a deep learning task. It involves 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 innovative approach allows polar codes to be directly utilized for MLC flash channels, significantly improving their reliability.

Career Highlights

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

Collaborations

Riqing Chen has collaborated with notable colleagues, including Pingping Chen and Zhen Mei. Their joint efforts contribute to the ongoing research and development in memory optimization technologies.

Conclusion

Riqing Chen's innovative methods in optimizing NAND flash memory demonstrate his commitment to advancing technology in this critical area. His contributions are paving the way for more reliable memory solutions in the future.