Computing and Advanced Memory Laboratory (CAML)
Research Field
Dr. Chao-Hung Wang received his Ph. D. degree in Materials Science and Engineering from the National Cheng Kung University (NCKU) in 2014. Later, He joined the Emerging Central Lab. at Macronix Inc. as an R&D engineer to start resistive random access memory (ReRAM) research during the years of 2015-2019. After that, He acquired a LEAP scholarship to U.C. Berkeley to develop 2D transition metal carbide electrode materials for renewable energy applications from 2019 to 2020. Meanwhile, He was also an advisor of ANAFLASH Inc., which is a startup dedicated to developing a low-power, higher-performance, and highly reliable NVM-based neuromorphic processor, under the Berkeley Science Fellow Program. Finished the LEAP program, He joined ATMD at TSMC as an R&D engineer, responsible for MEOL and BEOL interconnect metallization. Subsequently, Dr. Wang joined the Miin Wu School of Computing, NCKU as an Assistant Professor in August 2021. Dr. Wang's research interests include developing Brain-like computing using non-volatile memory, novel memory devices, and cutting-edge emerging memory materials. He has published more than 20 SCI and conference papers (sum of the times cited: 478, h-index: 13), as well as issued more than 8 patents as the inventor.
CAML focuses on memory-centric, brain-inspired computing technologies, encompassing bio-inspired non-volatile memory materials and devices, computing-in-memory circuit design, and spiking neural network algorithms. Our goal is to develop highly intelligent and energy-efficient computing systems that address catastrophic forgetting in modern AI systems and the von Neumann bottleneck.
Emerging Non-Volatile Memory Materials and Devices
Computing-in-memory: Devices and Simulation
Neuromorphic Computing: Memory Devices and Simulation
Next-Generation Semiconductor Materials, Devices, and Design
UAiTED 2023 Faculty Exchange Scholarship rewards
Ph. D., Department of materials science and engineering, NCKU
M.S., Department of materials science and engineering, NCKU
B.S., Department of materials science and engineering, NCKU
Job Description
- Design and develop bio-inspired non-volatile memory (NVM) materials and devices (e.g., RRAM/CBRAM, FeRAM) for neuromorphic applications
- Model, characterize, and optimize device-level behaviors for synaptic functionality
- Develop brain-inspired computing-in-memory (CIM) circuits and architectures for efficient data processing
- Co-design hardware and algorithms to bridge device, circuit, and system levels
- Design and implement spiking neural network (SNN) algorithms for edge intelligence tasks (e.g., classification, continual learning, sensory processing)
- Evaluate system performance in terms of energy efficiency, latency, scalability, and robustness
- Collaborate across interdisciplinary teams to prototype and validate neuromorphic systems
Preferred Intern Educational Level
Currently pursuing a Bachelor’s, Master’s, or Ph.D. degree in Electrical Engineering, Materials Science, Computer Engineering, or a related field
Skill sets or Qualities
- Strong background in one or more of the following areas:
- Non-volatile memory devices and materials
- Analog/mixed-signal or CIM circuit design
- Neuromorphic computing or spiking neural networks
- Experience with materials and device characterization techniques
- Experience with simulation tools (e.g., TCAD, SPICE, MATLAB, Python, PyTorch)
- Familiarity with hardware–software co-design is preferred
- Strong analytical, problem-solving, and communication skills