12^th International Conference & Exhibition on Biosensors & Bioelectronics October 25-26, 2019 | Vancouver | British Columbia | Canada

Biography:

Professor Xian-En Zhang received his B.Sc. (Biology) from Hubei University in 1982, M.Phil. (Microbiology) and Ph.D. (Biochemistry) later from the Institute of Virology and the Institute of Microbiology, Chinese Academy of Sciences (CAS), respectively. In 2015, he received an Honorary Degree of Doctor of Science from the University of Alberta, Canada. In 1993, he became a full professor at Wuhan Institute of Virology, CAS. He has been active in interdisciplinary sciences, specializing in biosensors, nanobiology and analytical pathogen microbiology. He has published more than 230 peer-reviewed papers and three books on biosensors. In April 2013, he completed his duty in MOST and joined the Institute of Biophysics (IBP), CAS, and is now a distinguished professor in the Center of Excellence of Macromolecules of IBP, CAS, while serve as the China’s representative to the APEC Chief Science Advisors and Equivalents Meeting.

His recent research directions include molecular/nano biosensors for biological events in live cells, viral/microbial elements-derived nano-biosensors, and portable biosensors for health care.

Abstract:

TBA

Biography:

Xiaohong (Ellen) Wang is full professor in Department of Microelectronics and Nanoelectronics, Tsinghua University, China, guest professor in ETH Zürich, Switzerland. She received her Ph.D. from Tsinghua University in Mechanical Engineering. As a visiting scholar, she did the research on micro SOFC in Stanford University from Nov. 2005 to Oct. 2006. She also had several months’ research experience in HKUST and UCLA in 2001 and 2007, respectively. Her research now is on the fields of MEMS/NEMS design and integration technologies, in particular on Power-MEMS, like micro fuel cells, micro supercapacitors, silicon-based lithium batteries, and biological microgenerators etc. She has served several international conferences as TPC/ETPC member, like IEEE-IEDM, IEEE-MEMS, Transducers, PowerMEMS etc. She was the General Co-Chair of IEEE-MEMS2016 held in Shanghai. She is an Associate Editor of IEEE/ASME JMEMS and PNG Microsystems & Nanoengineering, Editorial Advisory Board member of JMM.

Abstract:

The rapid development of wearable and implantable electronics has greatly increased the demand for energy harvesting and storage devices with similar physical properties and integration capability. Nowadays. Li-ion batteries have been being still mainstream as their high power and voltage output. Can we convert energy in the human body into electricity can provide a life-long power source to apply in various wearable electronics and potentially in implanted microdevices? Can we also store the energy into supercapacitors with the high charge/discharge rate, long cycle life, and high-energy efficiency? This speech will introduce the development of energy harvesting and storage on-chip for wearable or implantable applications.

Biography:

Prof. Hidekuni Takao, received his BS in 1993, MS in 1995 and Ph.D. degree in 1998 all from Toyohashi University of Technology, Japan. After he worked as a research fellow (PD) with JSPS, Japan in 1998, he joined faculty of Toyohashi University of Technology in 1999 as assistant professor, and was promoted to associate professor in 2005. He moved to Kagawa University, Japan in 2009, where he has served as a full professor since 2014. He is also holding the position of  director of Nano-Micro Structure Device Integrated Research Center in the same University. His research interests are high performance silicon MEMS sensors and systems and their applications to fine tactile sensing technology. Since 2015, he has been the representative of JST-CREST project on “nano-tactile sensing”. He acted as a technical committee member of IEEE MEMS 2012 Paris, 2013 Taipei, 2017 Las Vegass, and 2018 Belfast.

Abstract:

We, human has very sophisticated sense of touch on our fingertip skin. We can recognize and distinguish various and delicate difference of touch feelings obtained by sweep motion of fingertip on various kinds of materials and objects. Fingertip skin has the highest density of force and vibration receptors (Meissner’s Corpuscles and Merkel Cells) under the surface skin where fine pitch patterns of fingerprint are formed on. Human’s fingertip has a very high spatial resolution below 100µm and can recognize existence of 13nm-pitch patterns as reported recently.

In order to reproduce artificial sense of touch like human's fingertip, very high performances   on spatial resolution and sensitivity are required to tactile sensors. In this talk, silicon based MEMS tactile sensors with a ultra-high force and spatial resolutions are introduced and demonstrated. All the mechanical structures in the tactile sensor deice are made from “pure” single crystal silicon layer of SOI wafers. No elastomer/polymer structures are used in the sensing structure. The contactor parts of the tactile sensor have curved shape which is very similar to the cross-section of a fingerprint, and its suspension springs are designed similarly to a spring constant of human’s fingertip skin surface. In the latest version of our tactile sensors, six contactors with fingerprint-like shape are integrated at a pitch of 500µm to get high spatial resolution tactile images. Each fingerprint-like contactor reproduces vertical motion (by micro roughness) and horizontal motion (by frictional force) of a fingerprint closely under sweeping motion of fingertip in measurement. Spatial resolution of our tactile sensor reaches to sub-micron region, and its force resolution of friction is below 50µN. These performances are enough high to analyze surface touch feelings of “Hair surface condition”, “Skin condition”, and “Touch feeling of various papers and clothes” like human fingertip. Machine learning based on deep neural network has been applied to the signal from the high resolution tactile sensors. As a result, 10 kinds of “cloth” samples have been discriminated at a correct percentage of 99% successfully. Combination of high resolution tactile sensor and deep neural network is a strong approach to reproduce human fingertip sensation by state-of-the-art device electron device technology.