Hongyu An

Degree Objective: Ph.D

Resume: TBU

Research Interest:

  • Neuromorphic Engineering
  • Three-Dimentional Intergrated Circuit (3-D IC) Design
  • Machine Learning, Deep Learning, Reservoir Computiong and Reinforcement Learning
  • Artificial Neural Network
  • Nanoscale Device (Memristor)

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:

  • Neuromorphic Computing System Hardware Implementation using Memristor and Monolithic 3D Integration 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
  • Developed a segmentation based circuit model to study the noise coupling among multiple signal traces over a split reference plane
  • Developed a fast estimation approach to calculate via crosstalk in multi-layer, multi-via transitions using MatLab
  • Investigated the characteristic impedance for a trace in proximity to a meshed reference plane

Peer-Reviewed Publications:

Awards:

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

Professional Oral Presentations:

  • “3D Memristor-based Adjustable Deep Recurrent Neural Network with Programmable Attention Mechanism” in Neuromorphic Computing Symposium 2017: Architectures, Models, and Applications, held by Oak Ridge National Laboratory, 2017
  • “When Energy Efficient Spike-Based Temporal Encoding Meets Resistive Crossbar: From Circuit Design to Application” in Neuromorphic Computing Symposium 2017: Architectures, Models, and Applications, held by Oak Ridge National Laboratory, 2017
  • “Opportunities and Challenges on Nanoscale 3D Neuromorphic Computing System” in IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, 2017
  • “Modeling and Analysis of Neuronal Membrane Electrical Activities in 3D Neuromorphic Computing System” in IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, 2017
  • “Electrical Modeling and Analysis of 3D Synaptic Array using Vertical RRAM Structure” in Quality Electronic Design (ISQED), 2017 18th International Symposium on, 2017

Professional Services:

Journal/Conference Reviewer:

  • IEEE International Conference on Nanotechnology (2017-)
  • IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity (2017-)
  • Integration, the VLSI Journal (2017-)
  • Nano Communication Networks (2017-)
  • IET Cyber-Physical Systems: Theory & Applications (2017-)

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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