Plenary Speakers

Chair Prof. Ying-Hao Chu
Associate Vice President for Research and Development
Director of NTHU CNMM
Department of Materials Science and Engineering
National Tsing Hua University, Taiwan

Title of Plenary Speech
Epitaxial Growth of Bi₂O₂X Films and Related Heterostructures

Abstract of Plenary Speech
The pursuit of high-performance electronic devices has driven the research focus towards 2D semiconductors with high electron mobility and suitable bandgap. Previous studies have demonstrated that bismuth oxycompound (Bi₂O₂X, X=S, Se, Te, BOX) has remarkable physical properties, such as native oxide layer and ultrahigh carrier mobility, facilitating the development of advanced electronic devices in the sub-silicon era. However, the understanding of the growth of BOX is still unclear, and the methods to control BOX' s physical properties should be further explored. Thus, in our lab, the epitaxy of Bi₂O₂Se (BOSe) and Bi₂O₂S (BOS) can be obtained to realize the growth mechanism and secure higher crystallinity. The intrinsic transportation property and photoelectricity of BOX will also be investigated. Furthermore, various control methods have been demonstrated on BOX, including compositional modification, doping process, and non-volatile modulation. With these efforts, the controlled methods provide pathways to manipulate the electrical properties of BOX and corresponding devices, further advancing this material system for development in the next-generational electronics.

GlobalFoundries Chair Prof. Chengkuo Lee
Center for Intelligent Sensors and MEMS
Department of Electrical and Computer Engineering
National University of Singapore, Singapore

Title of Plenary Speech
CMOS Photonics – Extending Si Photonics to In-Sensor Computing and Nanophotonic Sensing Platforms

Abstract of Plenary Speech
In the rapidly evolving field of artificial intelligence of things (AIoT), a key challenge is to process sensing signals efficiently while minimizing latency and power consumption. Traditional microelectronic systems face limitations in speed and energy efficiency, prompting the exploration of photonic integrated circuits for their superior data transmission and computational capabilities. Silicon Photonic integrated Circuits (Si PIC) offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules. Furthermore, Si and AlN waveguides can manipulate light-matter interactions at the nanoscale, become an appealing CMOS Photonics platform for photonics neural networks (PNN) computation application and diversified biochemical and physical sensing applications with high sensitivity and small footprints. The multimodal spectroscopic sensory data is complex and shows huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, we bring the photonic sensor and processor into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor.
On the other hand, surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Recently, we have investigated the phonons in hyperspectral bioimaging by developing a synergistic plasmon-phonon system. We demonstrated that phonons, in synergy with plasmons, enhance the per-second spectral de-overlapping functionality in imaging dynamic reaction process involved with COVID viruses.

Keynote Speakers

Prof. Dr. Hieng-Kiat Jun
Department of Mechanical and Material Engineering
University Tunku Abdul Rahman, Malaysia

Title of Keynote Speech
Overview on the application of carbon quantum dots in energy storage devices

Abstract of Keynote Speech
In recent years, alternative battery devices like supercapacitors, and electric double-layer capacitors (EDLCs) have been receiving plenty of attention. This brief review focuses on supercapacitor fundamentals and the potential application of carbon quantum dots (CQDs) in the devices. Small nanoparticles of carbon, known as CQD, which are less than 10 nm in size and contain special qualities, have become an essential tool for known specific delivery, biological research, and many therapeutic uses. The purpose of this review is also to assemble the recent research on CQDs synthesis with specific focus to biomass of coffee grounds, their characterization methods, and recent progress of CQDs in energy devices. For the synthesis of CQDs, two different types of synthesis methods i.e., a top-down approach and a bottom-up approach—are employed. The laser ablation method, electrochemical method, and arc-discharge method are examples of top-down techniques. The acidic oxidation, microwave-assisted method, and hydrothermal method are examples of bottom-up approaches. CQDs are now receiving more interest from the energy storage sector as additives in electrode material due to their distinctive electrical characteristics and critical function in hosting multiple functional groups superficially. As a result, energy density of supercapacitors has increased with the widespread usage of CQDs in electrode materials.

Invited Speakers

Prof. Uma N. Dulhare
Computer Science & Artificial Intelligence Department
Muffakham Jah College of Engineering & Technology, India

Title of Invited Speech
Harnessing Deep Learning for Precision Diagnostics in Dermatology and Rheumatology

Abstract of Invited Speech
Deep learning is becoming essential in revolutionizing medical diagnostics and personalized care for skin lesions and rheumatoid arthritis. As a subset of AI becomes more integrated into healthcare, it significantly improves diagnostic accuracy, treatment optimization and research advancement in dermatology and rheumatology.
A proposed framework leveraging convolutional neural networks (CNNs) with hybrid architecture is designed to detect and classify various skin lesions including melanoma and nodules with enhanced precision, early intervention and better prognosis. Furthermore, deep learning is providing new insights into rheumatoid nodules by integrating multimodal data such as imaging and patient histories, leading to a better understanding of nodule progression and personalized treatment for autoimmune conditions like rheumatoid arthritis (RA).
Experimental studies shows a pathway of relationship between skin lesions and rheumatoid arthritis progression. These finding shed light on how inflammation can contribute to secondary skin conditions, recommending disease management and treatment strategies.