Hongyu An

Degree Objective: Ph.D

Research Interest:

  • Neuromorphic Engineering/Computing
  • Three-Dimensional Integrated Circuit (3-D IC) Design
  • Emerging Nanoscale Device Design (Memristor)
  • Machine Learning, Deep Learning, Reservoir Computing, Recurrent Neural Networks
  • Artificial Intelligence
  • Electromagnetics

Education:

  • MS, Electrical Engineering, Missouri University of Science and Technology, Rolla, MO, USA
  • BS, Electrical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China

Research Experiences:

Virginie Tech

  • The extremely scalable distributed neuromorphic computing system design using memristor-based devices and monolithic three-dimensional (3D) integration technology
  • Three-dimensional Memristor-based Associative Neuromorphic Computing Circuit and System Design 

Missouri University of Science and Technology

  • Studied the noise coupling mechanism from switching power supply to signal nets by building a hybrid model using SPICE and HFSS
  • Studied the effects of GND via patterns as well as the board dimensions on single-ended signal via transitions in the frequency domain
  • Investigated via crosstalk as a function of PCB board layers, layer thickness, board dimensions
  • Developed a fast estimation approach to calculating via crosstalk in multi-layer, multi-via transitions using MatLab

Peer-Reviewed Publications:

Examination

Awards:

  • NSF Student Travel Fellowship Reward on IEEE International Conference on Nanotechnology (IEEE NANO 2017)

Professional Invited Talks:

Professional Services:

  • Journal/Conference Reviewer:
    • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
    • IEEE Transactions on Circuits and System I: Regular Papers (TCAS)
    • IEEE International Conference on Nanotechnology (TNANO)
    • IEEE International Symposium on Quality Electronic Design (ISQED)
    • IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity (EMC-SIPI)
    • Design Automation Conference (DAC)
    • ACM Great Lakes Symposium on VLSI (GLSVLSI)
    • Integration, the VLSI Journal
    • Emerging Technologies in Computing
    • Nano Communication Networks
    • IET Cyber-Physical Systems: Theory & Applications

Relevant Courses:

  • Signal Integrity in High-Speed Digital
  • Mixed Signal Design
  • Advanced Electromagnetic
  • Antenna & Propagation
  • Advanced RF Design
  • Grounding & Shielding
  • Digital Signal Processing
  • Computational Electromagnetic

Skills:

  • Programming: MATLAB, C/C++, LabView, PHP, Python, Java
  • Software: HFSS, CST, PSPICE, ADS, Hyperlynx, Q3D, Q2D, HSPICE, Altium
  • Instruments: Network Analyzer, TDR, Spectrum Analyzer, Oscilloscope, Signal generator
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