Hebei University - China
Author Profile
Scopus
🎓 Early Academic Pursuits
Prof. Dr. Xiaobing Yan embarked on his academic journey with a strong foundation in electronics and advanced computing, ultimately leading to a distinguished career in academia and research. His early academic pursuits were marked by rigorous training in electrical engineering and cutting-edge technological innovations. As a professor and doctoral supervisor at Hebei University, he has dedicated his career to pushing the boundaries of modern electronics, particularly in the field of ferroelectric memristors and neural networks.
💼 Professional Endeavors
With an illustrious career spanning multiple prestigious institutions and associations, Prof. Dr. Xiaobing Yan has established himself as a leading figure in electronic engineering and neural computing. He holds a senior membership in the IEEE Association of America and the China Electronics Society and serves as the director of the China Youth Association for Science and Technology. His expertise in ferroelectric memristors and neural networks has made significant contributions to artificial intelligence, neuromorphic computing, and next-generation memory storage technologies.
🔬 Contributions and Research Focus
Prof. Dr. Xiaobing Yan's research primarily focuses on ferroelectric memristors and neural networks, which are crucial for developing efficient, high-speed, and energy-saving computational architectures. His work has pioneered novel approaches to neuromorphic computing, significantly enhancing the performance of artificial intelligence models. By integrating ferroelectric memristors into neural networks, he has contributed to the development of intelligent hardware capable of mimicking human brain functions. His research breakthroughs have been widely acknowledged in both national and international scientific communities.
🌍 Impact and Influence
Prof. Dr. Xiaobing Yan’s influence extends beyond academia, shaping policies and technological advancements at national and global levels. He has been recognized with numerous prestigious awards, including the Young Scholars of National Major Talent Project, the Top Young Talents of the 10,000 Talents Plan of the Central Organization Department, and the Huo Yingdong Young Teacher Award of the Ministry of Education. His work in ferroelectric memristors and neural networks has influenced emerging computing paradigms, paving the way for more advanced and efficient artificial intelligence applications.
🏆Academic Cites
As a leading researcher, Prof. Dr. Xiaobing Yan has published extensively in high-impact journals, earning a substantial number of academic citations. His pioneering research on ferroelectric memristors and neural networks has been widely referenced, demonstrating its importance in advancing neuromorphic computing and AI-driven technologies. His work continues to inspire future research in electronic materials and intelligent computing.
🌟 Legacy and Future Contributions
Prof. Dr. Xiaobing Yan’s legacy is deeply rooted in innovation, mentorship, and groundbreaking research. As a top-tier scientist and educator, he remains committed to mentoring young researchers and guiding the next generation of scholars. His future contributions are expected to further revolutionize neuromorphic computing and artificial intelligence, solidifying his place as a trailblazer in ferroelectric memristors and neural networks. Through his relentless pursuit of knowledge and technological advancements, his impact on modern electronics and AI will continue to shape the field for years to come.
📝Ferroelectric Memristors and Neural Networks
Prof. Dr. Xiaobing Yan’s groundbreaking work in ferroelectric memristors and neural networks has redefined the future of computing and AI-driven systems. His extensive research on ferroelectric memristors and neural networks has contributed to the advancement of energy-efficient and high-performance neuromorphic hardware. As a leading figure in ferroelectric memristors and neural networks, his legacy continues to shape the next wave of technological innovation.
Notable Publication
📝Physical Unclonable In-Memory Computing for Simultaneously Protecting Private Data and Deep Learning Models
Authors: W. Yue, K. Wu, Z. Li, R. Huang, Y. Yang
Journal: Nature Communications (2025)
Focus: Secure in-memory computing to protect both private data and deep learning models from attacks.
Citations: 0
📝Memristor-Based Feature Learning for Pattern Classification
Authors: T. Shi, L. Gao, Y. Tian, X. Yan, Q. Liu
Journal: Nature Communications (2025)
Focus: Memristor-based neuromorphic computing for efficient pattern recognition.
Citations: 0
📝In Situ Training of an In-Sensor Artificial Neural Network Based on Ferroelectric Photosensors
Authors: H. Lin, J. Ou, Z. Fan, X. Gao, J. Liu
Journal: Nature Communications (2025)
Focus: Ferroelectric photosensors for AI training directly within sensors, reducing energy consumption.
Citations: 2
📝Ultra Robust Negative Differential Resistance Memristor for Hardware Neuron Circuit Implementation
Authors: Y. Pei, B. Yang, X. Zhang, S. Li, X. Yan
Journal: Nature Communications (2025)
Focus: Development of a highly stable memristor with negative differential resistance for neuromorphic circuits.
Citations: 0
📝Regulating the Growth Mechanism of Kesterite Thin Films with Single-Target Selenium-Free Annealing by Introducing a Suitable Buried Buffer Layer at the Bottom
Authors: Q. Zhou, Y. Cong, T. Wu, C. Gao, W. Yu
Journal: Chemical Engineering Journal (2025)
Focus: Optimization of kesterite thin films for solar cells through a novel annealing approach.
Citations: 0