PhD Student, University of Virginia, United States
Jun Yin is a dedicated Ph.D. candidate in Electrical Engineering at the University of Virginia, with a robust academic and professional background in VLSI design, low-power circuits, and memory systems. With experience spanning top research institutions and the semiconductor industry, Jun’s work bridges theoretical research and practical innovation, focusing on emerging chip designs and system-level efficiency. His efforts have already earned recognition in international conferences and high-impact journals, making him a rising figure in the field of electrical and computer engineering.
Professional Profile
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🎓 Education Background:
Jun holds a Ph.D. (2021–2025, GPA 3.925/4.0) and a Master’s degree (2021–2023, GPA 3.925/4.0) in Electrical Engineering from the University of Virginia, USA. Before that, he completed his M.Sc. in Materials Science at Tsinghua University (2016–2019, GPA 3.77/4.0), one of China’s top institutions. He began his academic journey with a B.Eng. in Material Engineering from Qinghai University (2012–2016), where he graduated with an impressive GPA of 89/100.
💼 Professional Experience:
Jun is currently interning at MediaTek USA Inc., Austin, TX, working on advanced memory circuit design using leading technologies such as TSMC N3E and N2P. His work includes SRAM library generation, margin verification, and design optimization using tools like NanoTime and XA. Previously, Jun contributed to FPGA and ASIC design projects at the University of Virginia and worked on neural network accelerators at UMass Amherst, achieving notable results such as a 99.5% fabrication yield and 93.63% classification accuracy. He has also served as a teaching assistant for graduate-level courses in digital design.
🏆 Awards and Honors:
Jun has received several honors for his outstanding contributions to research, including being named a Young Fellow at the 58th Design Automation Conference (DAC) in 2021. He also earned the Dean’s Fellowship at UMass Amherst in 2020, recognizing his academic excellence and research potential in the field of engineering.
🔬 Research Focus:
Jun’s research spans VLSI physical design, low-power SRAM circuits, RF energy harvesting systems, and AI hardware accelerators. He has developed innovative techniques in leakage suppression and impedance matching for IoT and CRFID applications. His current work at the University of Virginia focuses on system-on-chip solutions for energy-constrained environments and has led to publications in top IEEE conferences like ISCAS and ISQED. Additionally, his past work on memristor-based systems contributed to high-impact journals and breakthroughs in neural hardware.
🔚 Conclusion:
Jun Yin exemplifies a new generation of interdisciplinary researchers who blend academic excellence with industry-ready skills. With a proven publication record, practical experience in advanced semiconductor technologies, and a passion for circuit innovation, he is poised to make significant contributions to the future of low-power and intelligent electronic systems.
📚 Top Publications with Citation Details:
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A Low Power SRAM with Fully Dynamic Leakage Suppression for IoT Nodes – IEEE ISQED, 2023
Cited by: 2 articles
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Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity – Advanced Functional Materials, 2018
Cited by: 181 articles
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Competition between Metallic and Vacancy Defect Conductive Filaments in a CH3NH3PbI3-Based Memory Device – Journal of Physical Chemistry C, 2018
Cited by: 157 articles
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Guiding the growth of a conductive filament by nanoindentation to improve resistive switching – ACS Applied Materials & Interfaces, 2017
Cited by: 135 articles
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Performance‐enhancing selector via symmetrical multilayer design – Advanced Functional Materials, 2019
Cited by: 88 articles
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A fully hardware-based memristive multilayer neural network – Science Advances, 2021
Cited by: 78 articles
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Modulating metallic conductive filaments via bilayer oxides in resistive switching memory – Applied Physics Letters, 2019
Cited by: 55 articles
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Phase-change nanoclusters embedded in a memristor for simulating synaptic learning – Nanoscale, 2019
Cited by: 32 articles