Plenary Speaker

Education

• Doctor of Engineering, Tohoku University

Research Interests

MEMS, Nano and micro fabrication, Electronics packaging, Surface modification and Bio compatible materials.

My goal is to construct this “Science of Nano and Micro Devices”, and it won’t be possible unless I establish these five main technologies, MEMS, Nano and micro fabrication, Electronics packaging, Surface modification, and Bio compatible materials.

Background

Dr. Mizuno received his PhD in engineering from Tohoku University, where he conducted research on silicon capacitive sensors that can simultaneously detect acceleration and angular acceleration.

Since then, he has been conducting research on organic EL using liquid semiconductors, SAW devices using low-temperature bonding technology, 3D electronics packaging technology, low-damage surface treatment machines, and recently, implant materials and regenerative medicine of epithelial mucosa in collaboration with medical engineering.

Education

• BA, Cornell University
• MS, PhD, Brown University

Background

Dr. Huang received his BA in physics from Cornell University, and his MS and PhD degrees in engineering from Brown University. He received postdoctoral research training at the Biomedical Engineering Department at Tufts University, working on projects of light scattering diagnostic techniques and flow cytometry for diseased tissues and cancerous cells. He has authored numerous research articles and book chapters on image-based velocimetry techniques and nanoscale transport phenomena. His current research interests encompass the field of micro- and nanofluidics, including optical sensing techniques, micro- and nanoscale mass transport, lab-on-a-chip systems for biomedical applications, biologically-inspired microfluidic technologies, adhesion dynamics of cancerous cells and optical manipulations of microorganisms. Dr. Huang is a member of the ASME, the APS and the OSA, and was the topic organizer for the Microfluidics Forum at the ASME IMECE 2009-2011.

Education

• Ph.D., University of California-Riverside

Research Interests

• Biosensor
• Gas Sensor
• Bionanotechnology
• Environmental Biotechnology
• High Temperature Nanomaterials based Sensing
• Microfluidic-based Biosensor

Education

• Ph.D., Chemical Engineering, Princeton University

• BS., Chemical Engineering, California Institute of Technology

Research Interests

• Reaction engineering
• Control
• Fluid mechanics
• Soft materials
• Biosensing

Education

• B.S. Materials Science and Engineering, Jilin University, China

• M.E. Chemical Engineering, Hunan University, Chinay

• Ph.D. Materials Science and Engineering, Georgia Institute of Technology

Background

Prof. Xudong Wang is the Grainger Institute for Engineering Professor in the department of Materials Science and Engineering at University of Wisconsin – Madison, and the Energy & Sustainability thrust Leader at the Grainger Institute for Engineering.

Dr. Wang received his PhD degree in Materials Science and Engineering from Georgia Tech in 2005. His current research interests include studying the growth mechanisms and developing assembly techniques of oxide nanostructures; developing advanced nanomaterials and nanodevices for mechanical energy harvesting from human activities for biomedical applications; and understanding the coupling effect between piezoelectric polarization and semiconductor functionalities.

He has won number of prestigious national and international awards, including PECASE, NSF CAREER Award, DARPA Young Faculty Award, etc. He has published more than 160 papers on peer-reviewed journals, including Science, Nature, Nature Energy, etc. His current h-index is 79.

Abstract

Nanogenerator (NG) has been considered as a promising solution to biomechanical energy harvesting inside human body.

So far, many technology innovations have advanced the NG technology toward a broad range of biomedical applications. Fundamentally, materials design and engineering draw the boundary where this technology may advance. In this talk, I introduce our most recent development of piezoelectric materials and composites that are particularly designed for implantable NG applications. First, I present our wafer-scale approach to creating piezoelectric biomaterial thin films based on γ glycine crystals.

The self-assembled sandwich film structure enabled both strong piezoelectricity and largely improved flexibility. Then, new ferroelectric composites are presented as a new material used in 3D printing for directly manufacturing of piezoelectric architectures with tunable piezoelectric and mechanical properties. Toward the end, novel applications of implantable piezoelectric materials are introduced, which enable the closed-loop electrostimulations for many biomedical therapeutics.

Education

• B.S., M.S., Electrical Engineering, Seoul National University, Seoul, Korea
• Ph.D Electrical Engineering, University of Michigan, Ann Arbor

Background

Dr. Joungho Kim received B.S. and M.S. degrees in electrical engineering from Seoul National University, Seoul, Korea, in 1984 and 1986, respectively, and Ph.D degree in electrical engineering from the University of Michigan, Ann Arbor, in 1993. In 1996, he moved to KAIST (Korea Advanced Institute of Science and Technology). He is currently professor at electrical engineering department of KAIST and a joint faculty member of KAIST AI college. Also, he serves as the director of Samsung-KAIST Industry Collaboration Center.

He is an IEEE fellow.Research is focusing on the developing machine learning and reinforcement learning methodologies for the high-speed IC, package, and PCB designs including 3D IC, HBM and next generation AI computer modules. In addition, his research is centered on signal integrity, power integrity, and electromagnetic interfaces for TSV, Interposer, and System-in-Package designs. He has authored and co-authored over 659 technical papers published at refereed journals and conference proceedings.

Abstract

Recently, we are facing a newly emerging industrial transition, named as ‘Digital Transformation’, which is heavily based on artificial intelligence (AI) and, big data, and cloud computing. It will bring whole changes in our society, industry, economics, and education systems. Especially, the emergence of this artificial intelligence is greatly aided by the availability of big data, and deep learning algorithms, as well as high-performance semiconductor and computing systems. Therefore, in order to support these transitions, we need to meet the demands for high performance semiconductor and computing systems with terabyte/s bandwidth.

In this presentation, we will introduce the machine learning (ML) based design and verification methodologies, for the design and optimization of next generation terabyte/s bandwidth semiconductor systems. In particular, we will explain Reinforcement Learning (RL) and Imitation Learning (IM) methods for the efficient and fast design and optimization processes. Especially, we will demonstrate these methods to satisfy the signal and the power integrity design requirements of interconnections, chips and packages. Eventually, the machine learning based processes will be applied to the whole semiconductor design tasks such as floor planning, circuit optimization, interconnect design, simulation, verification, and test processes. It will enable efficient and fast design processes in complex semiconductor and computing systems.