Qiujie Yuan | Reservoir Computing | Best Researcher Award

Mr. Qiujie Yuan | Reservoir Computing | Best Researcher Award

Qiujie Yuan at Nanjing University of Posts and Telecommunications, China

Qiujie Yuan is a graduate researcher in Integrated Circuit Science and Engineering at Nanjing University of Posts and Telecommunications, with a strong foundation in applied physics. With hands-on experience in phase transition engineering and flexible 2D semiconductor device research, he demonstrates a rare blend of interdisciplinary R&D, system-level thinking, and international collaboration. His work focuses on electrochemically modulated MoS₂ transistors and bio-inspired temporal processing, contributing toward advancements in low-power, intelligent sensing systems.

🔹Professional Profile:

Scopus Profile

Orcid Profile 

🎓Education Background

Nanjing University of Posts and Telecommunications

  • M.S. in Integrated Circuit Science and Engineering
    Sept 2023 – June 2026 (Expected)

  • B.S. in Applied Physics
    June 2019 – June 2023

  • Core Courses: Matrix Theory, CMOS Analog IC Design, Digital IC Analysis & Design, Power Devices & IC Design, Intelligent Sensors & Integrated Applications, Semiconductor Optoelectronics

💼 Professional Development

Graduate Researcher – National Key R&D Program
Focus: Flexible 2D Semiconductor Devices

  • Designed MoS₂-based electrochemical transistor processes, achieving 7.8 cm²·V⁻¹·s⁻¹ mobility (23% improvement)

  • Built a reservoir computing prototype with 92.4% classification accuracy using STDP optimization

  • Established in situ electrochemical characterization protocols and multiphysics testbeds for over 160 parametric analyses

  • Spearheaded interdisciplinary development from polymer electrolyte material synthesis (PVA/MXene) to system-level algorithm integration

Notable Project: “Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS2 Transistors for Time-Series Processing”

  • Introduced Li⁺-modulated phase transition for dynamic temporal processing

  • Reduced system hardware complexity by 60% with scalable virtual node design

  • Demonstrated successful material-to-system pipeline integration

🔬Research Focus

  • Phase transition engineering and ionic modulation

  • 2D semiconductors and electrochemical transistor design

  • Reservoir computing and bio-inspired neuromorphic systems

  • Integration of materials, device physics, and intelligent sensing algorithms

📈Author Metrics:

Qiujie Yuan is an emerging researcher with growing contributions in the fields of 2D semiconductors and neuromorphic computing. His recent work, “Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS₂ Transistors for Time-Series Processing,” has been recognized for its innovation in integrating material synthesis with algorithmic intelligence. Although early in his publication career, his research demonstrates strong potential for high-impact citation, particularly in interdisciplinary domains such as flexible electronics, intelligent sensing systems, and electrochemical device engineering. His work has attracted attention in both academic and industrial circles for its engineering applicability and novel use of ionic modulation in dynamic systems. As he continues publishing, his author metrics are expected to grow rapidly, especially given his involvement in national key R&D programs and international collaborations.

🏆Awards and Honors:

  • Second-Class Scholarship (Top 15%)

  • Third-Class Scholarship

  • Outstanding Graduate Cadre

  • CET-6 Score: 586 | National Graduate Entrance English Score: 84

  • Recognized for international technical collaboration across multilingual teams (Korea, Malaysia, Tunisia)

📝Publication Top Notes

1) Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS₂ Transistors for Time-Series Processing

Journal: Polymers
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Publication Date: April 2025
Type: Journal Article
Volume/Issue: Vol. 17, Issue 9
Article Number: 1178
DOI: 10.3390/polym17091178
Authors: Xiang Wan, Qiujie Yuan (邱杰 袁), Lianze Sun, Kunfang Chen, Dongyoon Khim, Zhongzhong Luo
Citations: 1 (as of current data)
Highlights:

  • Developed MoS₂-based electrochemical transistors with 23% improved field-effect mobility
  • Demonstrated a reservoir computing system with 92.4% classification accuracy
  • Reduced hardware complexity by 60% via scalable virtual node architecture

2) Performance Analysis of an Underwater Wireless Optical Communication Link with Lommel Beam

Journal: Physica Scripta
Publisher: IOP Publishing
Publication Date: 2024
Type: Journal ArticleAuthors: Yangbin Ma (Y. Ma), Xinguang Wang (X. Wang), Changjian Qin (C. Qin), Le Wang (L. Wang), Shengmei Zhao (S. Zhao)
Citations: 1 (as of current data)
Highlights:

  • Investigated the performance characteristics of Lommel beams in underwater optical communication
  • Analyzed signal degradation and beam propagation dynamics
  • Offers insights for high-capacity underwater wireless systems

.Conclusion:

Mr. Qiujie Yuan exemplifies a rare, high-potential interdisciplinary researcher who effectively bridges materials engineering, semiconductor device design, and AI-inspired computing architectures. His ability to integrate fundamental research with system-level applications and international collaboration marks him as a deserving candidate for a Best Researcher Award, especially in fields related to neuromorphic computing, flexible electronics, and intelligent sensing systems.

With continued publishing, diversified application trials, and increased visibility, Mr. Yuan is poised to become a key innovator in next-generation low-power AI hardware. He is not only suitable but highly recommended for recognition through this award.

Jieru Song | Neuromorphic Computing | Best Researcher Award

Ms. Jieru Song | Neuromorphic Computing | Best Researcher Award

Jieru Song at Fudan University, China📖

Jieru Song is a Ph.D. candidate at Fudan University’s School of Microelectronics, specializing in neuromorphic computing and optoelectronic devices. With a Bachelor’s degree in Microelectronics Science and Engineering from Nanjing University, he has contributed to advancing self-powered heterojunction devices and optoelectronic memristors for reservoir computing. His work focuses on the integration of sensing, storage, and computing, with a particular emphasis on energy-efficient solutions for neuromorphic applications. Jieru’s research has resulted in significant innovations in signal processing, energy-efficient computing, and artificial neural networks.]

Profile

Scopus Profile

Education Background🎓

  1. Bachelor’s Degree in Microelectronics Science and Engineering, Nanjing University
  2. Ph.D. (Ongoing), School of Microelectronics, Fudan University (Research in optoelectronic memristors and neuromorphic computing)

Professional Experience🌱

Jieru Song is currently pursuing his Ph.D. at Fudan University, focusing on the development of self-powered heterojunction devices and optoelectronic memristors for neuromorphic applications. His work integrates signal processing with energy-efficient computing solutions for reservoir computing. He has designed and fabricated devices for tasks such as speech and EMG signal classification, enhancing device performance through optimized fabrication techniques. His professional journey is characterized by contributions to innovative algorithms for signal conversion and classification, which have led to advancements in both hardware and software for computing systems.

Research Interests🔬

Her research interests include:

  • Neuromorphic Computing
  • Optoelectronic Memristors
  • Self-Powered Devices
  • Reservoir Computing
  • Energy-Efficient Computing Solutions
  • Signal Processing Algorithms

Author Metrics

Jieru Song has contributed significantly to the field of neuromorphic computing and optoelectronic devices, with his research published in several esteemed journals. Notably, his work on self-powered optoelectronic synaptic devices for both static and dynamic reservoir computing was published in Nano Energy in 2025, where it received widespread attention. Additionally, he has authored papers on photoelectric synaptic devices for neuromorphic computing, featured in IEEE Electron Device Letters (2024) and Journal of Semiconductors (2024). His publications reflect a strong focus on innovative devices for energy-efficient, integrated sensing, and computing systems, contributing valuable insights to the advancement of neuromorphic applications.

Awards and Honors

Jieru Song has been recognized for his groundbreaking contributions to neuromorphic computing and optoelectronic devices, including publication in leading journals and conferences. His research has garnered attention in the scientific community for advancing energy-efficient, integrated sensing, and computing systems, laying the foundation for future scalable technological solutions.

Publications Top Notes 📄

1. Self-powered optoelectronic synaptic device for both static and dynamic reservoir computing

  • Authors: Jieru Song, J. Meng, C. Lu, D.W. Zhang, L. Chen
  • Journal: Nano Energy
  • Year: 2025
  • Volume: 134
  • Article Number: 110574
  • DOI: [Link Disabled]
  • Abstract: This paper presents the development of a self-powered optoelectronic synaptic device that can efficiently operate for both static and dynamic reservoir computing tasks. The device has applications in neuromorphic systems and energy-efficient computation.

2. Reconfigurable Selector-Free All-Optical Controlled Neuromorphic Memristor for In-Memory Sensing and Reservoir Computing

  • Authors: C. Lu, J. Meng, Jieru Song, D.W. Zhang, L. Chen
  • Journal: ACS Nano
  • Year: 2024
  • Volume: 18
  • Issue: 43
  • Pages: 29715–29723
  • Citations: 1
  • DOI: [Link Disabled]
  • Abstract: This study introduces a reconfigurable, selector-free all-optical controlled neuromorphic memristor designed for in-memory sensing and reservoir computing. This novel device has significant implications for neuromorphic computing systems.

3. InGaZnO-based photoelectric synaptic devices for neuromorphic computing

  • Authors: Jieru Song, J. Meng, T. Wang, D.W. Zhang, L. Chen
  • Journal: Journal of Semiconductors
  • Year: 2024
  • Volume: 45
  • Issue: 9
  • Article Number: 092402
  • Citations: 1
  • DOI: [Link Disabled]
  • Abstract: This article explores InGaZnO-based photoelectric synaptic devices, focusing on their application in neuromorphic computing. The devices are designed to enhance performance for computational tasks like image recognition and signal classification.

4. Fluorite-structured antiferroelectric hafnium-zirconium oxide for emerging nonvolatile memory and neuromorphic-computing applications

  • Authors: K. Xu, T. Wang, J. Yu, D.W. Zhang, L. Chen
  • Journal: Applied Physics Reviews
  • Year: 2024
  • Volume: 11
  • Issue: 2
  • Article Number: 021303
  • Citations: 3
  • Abstract: The paper investigates the use of fluorite-structured antiferroelectric hafnium-zirconium oxide in nonvolatile memory and neuromorphic computing applications. The material’s properties are optimized for energy efficiency in memory storage and computing systems.

5. Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing

  • Authors: J. Meng, Jieru Song, Y. Fang, D.W. Zhang, L. Chen
  • Journal: ACS Nano
  • Year: 2024
  • Volume: 18
  • Issue: 12
  • Pages: 9150–9159
  • Citations: 7
  • Abstract: This research introduces ionic diffusive nanomemristors that exhibit dendritic competition and cooperation functions, designed for ultralow voltage neuromorphic computing. These memristors are key for advancing neuromorphic computing systems with minimal energy consumption.

Conclusion

Jieru Song has exhibited a strong and consistent track record of groundbreaking research in the emerging field of neuromorphic computing. His innovative work in self-powered optoelectronic devices, memristors, and energy-efficient computing solutions positions him as a leader in the field. His ability to combine technical ingenuity with practical applications has already begun to influence both the academic and technological communities.

Given the impressive impact of his research and its potential for long-term contribution to AI, signal processing, and sustainable computing, Jieru Song is highly deserving of the Best Researcher Award. With further development in industry collaborations and expanded research in cross-disciplinary applications, he can continue to push the boundaries of neuromorphic computing and its practical applications.