Plenary Speakers

Chair Prof. and Department Chair, Ying-Hao Chu
Department of Materials Science & Engineering,
National Tsing Hua University, Taiwan

Title of Plenary Speech
Bismuth-Based 2D Semiconductors: A New Era for Silicon-Integrated Electronics and Functional Native Interfaces

Abstract of Plenary Speech
Two-dimensional (2D) bismuth oxychalcogenides (Bi2O2X), where X = S, Se, Te, have emerged as an extraordinary material platform for next-generation nanoelectronics owing to their high carrier mobility, moderate band gaps, and exceptional ambient stability. In this plenary talk, we present our systematic journey in tailoring the electronic landscapes of these ternary non-van der Waals semiconductors, transitioning from fundamental transport optimization to monolithic silicon integration and the discovery of functional native oxide interfaces. Previously, we established robust, nonvolatile modulation of Bi2O2Se channel conductivity by integrating a ferroelectric layer to electrically tune the semiconductor's Fermi level and band alignment. To resolve the long-standing challenge of p-type 2D electronics, we developed an area-selective, low-temperature pulsed laser doping process using Zn2+ substituents to replace Bi3+ sites, successfully achieving p-type polarity modulation and demonstrating high-performance vertical p-n homojunctions and CMOS logic inverters. Furthermore, through monotonous sulfur-selenium alloying, the carrier transport was optimized, yielding a peak field-effect electron mobility of 215 cm2V-1s-1 and an on/off ratio of 106 at a critical S:Se ratio of 4:6 with a Bi2SeO5 gate dielectric. Building upon these achievements, we report three major, yet-to-be-published advancements that push these materials toward practical semiconductor applications. First, addressing the integration bottleneck on industry-standard silicon, we demonstrate the direct epitaxial growth of tetragonal Bi2O2Se on silicon (Si) substrates enabled by a thin epitaxial SrTiO3 buffer layer. This monolithic integration bypasses the degradation typically induced by conventional transfer techniques, achieving high-quality single-crystalline domains on Si. Second, we unveil the experimental evidence of electrically switchable ferroelectric polarization in Bi2SeO5 at room temperature. Conventionally utilized as a passive, high-k native gate dielectric or passivation layer, we demonstrate that the non-centrosymmetric structural distortion in Bi2SeO5 natively hosts robust, switchable polarization. This discovery enables an "all-native" ferroelectric field-effect transistor architecture with atomically sharp, defect-free interfaces. Third, to elucidate the fundamental mechanism of this transformation, we present our comprehensive in-situ investigation tracking the continuous thermal oxidation of Bi2O2Se into ferroelectric Bi2SeO5. Utilizing real-time, in-situ high-resolution transmission electron microscopy, in-situ X-ray photoelectron spectroscopy, and in-situ Raman spectroscopy, we have mapped the dynamic oxygen diffusion pathways and localized structural reconstruction with atomic precision. These findings pave a new way for fabricating self-assembled, silicon-compatible 2D ferroelectric electronics.

GlobalFoundries Chair Prof. Cheng-Kuo Lee
Center for Intelligent Sensors and MEMS, National University of Singapore, Singapore
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
National Centre for Advanced Integrated Photonics, Singapore

Title of Plenary Speech
CMOS Photonics Circuits Enabled Photonic Neural Networks and Edge AI Sensing

Abstract of Plenary Speech
With the growing demand for energy-efficient AI applications, the rapid development of photonic neural networks (PNNs) represent a strategy for integrating photonics with AI computing architectures. PNNs harness multiple degrees of freedom of light, i.e., wavelength, amplitude, phase, mode, and polarization, to encode and manipulate large volumes of data in parallel. Through optical multiplexing, PNNs enable high-throughput, low-latency computation while reducing energy consumption compared with electronic counterparts. The development of AlN/Si-based CMOS photonics circuits have been developed as a near-sensor edge computing (NSEC) platform which pushes the boundary of real-time AI by combining electro-optic microring resonators (MRRs) and thermo-optic interferometers (MZIs) to achieve low-latency neural computation directly at the sensing layer. Demonstrated with high accuracy in multimodal gesture and gait classification tasks (96.77% and 98.31%, respectively), and achieving latency under 10 ns with energy consumption below 0.34 pJ, such platforms are paving the way for privacy-preserving, always-on AI hardware for healthcare, robotics, and immersive interaction systems. Overall, the fusion of AI-enhanced photonic sensing, on-chip neuromorphic computing, and flexible sensor integration represents a paradigm shift for future edge AI sensing systems. As optical edge computing continues to mature, it will become a cornerstone in the transition from centralized cloud AI to energy-efficient, responsive, and context-aware edge intelligence. Looking ahead, the convergence of AI, photonic integration, and edge computing will catalyze the next wave of intelligent systems that are no longer confined to centralized data centers or limited by power and latency bottlenecks. Future edge AI sensing architectures will evolve toward using CMOS photonics circuits enabled computing and sensing integrated chips which can achieve perception, inference, and adaptation in real time. Ultimately, the long-term vision is a world where every object, environment, and human interaction is seamlessly sensed, interpreted, and enhanced in real time.

Keynote Speakers

Prof. Yu-Lin Shen
Department of Mechanical Engineering
University of New Mexico,USA

Title of Keynote Speech
On the Applicability of Wafer Curvature Measurement to Determine Stress in Porous Thin Films

Abstract of Keynote Speech
The widespread use of Stoney's equation to determine thin-film stress from wafer curvature measurement assumes a continuous, defect-free thin film. However, its applicability to thin films with internal heterogeneity – such as voids and microcracks – remain unclear since these microscopic features generate highly nonuniform stress and deformation fields in the thin film. This problem can be tackled with systematic numerical simulations. We employed finite element analyses to investigate void-containing thin films bonded to thick substrates under thermal mismatch loading. By comparing curvature-derived average film stresses with direct mechanical simulations of standalone films, the validity of Stoney's equation in both the elastic and plastic regimes can be assessed. Our results demonstrate that Stoney's equation reliably predicts the average film stress for elastic and early plastic deformation, regardless of void distribution and porosity. On the other hand, with extensive plasticity, the substrate constraint alters local deformation behavior, leading to discrepancies between curvature-converted stress and the intrinsic flow stress of the porous film. These findings provide critical guidance for interpreting curvature-based stress measurements and highlight the role of interfacial constraint in modulating damage evolution.

Invited Speakers

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

Title of Invited Speech
To be announced

Abstract of Invited Speech
To be announced

Assoc. Prof. Wei-Sea Chang
Department of Materials Science and Engineering
National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Title of Invited Speech
MAGNETRON-SPUTTERED HIGH-ENTROPY CERAMIC THIN FILMS FOR DIELECTRIC CAPACITOR: A PROCESS DEVELOPMENT STUDY

Abstract of Invited Speech
High-entropy ceramics (HECs), which incorporate five or more cations into a single-phase solid solution, have attracted great interest in dielectric property tuning and energy storage applications due to their compositional flexibility. However, depositing high-entropy ceramic thin films via magnetron sputtering remains challenging, particularly in achieving stoichiometric transfer from the ceramic target, controlling phase formation, and maintaining film integrity. This work presents ongoing efforts to develop a reliable magnetron sputtering process for high-entropy ceramic thin films intended for capacitor and dielectric storage applications. Key process parameters, including sputtering power, working pressure, temperature and substrate conditions, are being systematically investigated to establish their influence on film composition, phase stability, and microstructure. These preliminary findings are guiding process refinement toward better film quality for subsequent dielectric evaluation, laying the groundwork for future structure–property studies in high-entropy ceramic thin films for dielectric energy storage.

Assoc. Prof. Jyoti Jaiswal
Department of Physics, Rajiv Gandhi University
Rono-Hills, Doimukh, Arunachal Pradesh, India

Title of Invited Speech
To be announced

Abstract of Invited Speech
To be announced

Prof. Chao-Cheng Kaun
Research Center for Applied Sciences, Academia Sinica, Taiwan

Title of Invited Speech
Computational Modeling of Nanoelectronics and Emerging Materials

Abstract of Invited Speech
Using first-principles calculations based on density functional theory (DFT) and the non-equilibrium Green's function method, we investigate performance of transition-metal-dichalcogenide (WSe2, WS2, and MoS2) channeled field-effect transistors and enhanced low-temperature photoluminescence in α-CsPbI3/WS2 heterostructures for nanoelectronic applications. Effects of biasing, contacting, configuring and quantum interfering are addressed. Using first-principles calculations based on DFT, we explore the bandgap tuning of NiFeV layered double hydroxides for optoelectronic and catalytic devices. Effects of compositing and cation ordering are identified. Moreover, we study the efficiencies of polymer-protected perovskite quantum dot films for LED backlighting and polymer-promoted solid-state electrolytes for lithium metal batteries. Effects of polymer-adsorbing and -interfacing are highlighted.

Prof. Ngoc Dang Khoa Tran
Faculty of Mechanical Engineering
Industrial University of Ho Chi Minh City, Vietnam

Title of Invited Speech
Data-Driven Modeling and Optimization of Surface Roughness and Vibration in End Milling Using a Wireless Sensor Tool Holder

Abstract of Invited Speech
Processing difficult-to-cut materials with optimal surface integrity is essential for advanced manufacturing. Tool vibration degrades 12X18N10T stainless steel machining. The best cutting settings for this tough material require measuring vibrations. Traditional wired vibration-monitoring systems often lose signal quality due to the distance between sensors and the cutting zone. This project collects high-quality, real-time data from a Bluetooth-enabled wireless vibration- monitoring device in the tool holder. A study used a 23 factorial design and ANOVA to analyze the impact of cutting speed (v), feed per tooth (S), and depth of cut (t) on surface roughness (Ra) and vibration amplitude (A). The model showed high reliability (R2 ≥ 90%), with cutting speed (v) significantly affecting vibration amplitude (A) and feed per tooth (S) significantly affecting surface roughness (Ra). The optimal settings for Derringer’s desirability multi-objective optimization method were (v 45 mm/min), (S 0.02 mm/tooth), and (t 0.2 mm), resulting in an approximate surface roughness (Ra) of 3.07 µm and a vibration amplitude (A) of 0.01196 mm. The study also establishes “feasible machining zones” with (Ra ≤ 0.4 µm and A ≤ 0.04 mm) for intelligent, adaptive control in Industry 4.0 compatible milling systems.

Assoc. Prof. Dr. Teeranoot Chanthasopeephan
Mechanical Engineering Department
King Mongkut's University of Technology Thonburi, Bangkok, Thailand

Title of Invited Speech
From Flexibility to Intelligence: Advancements in Soft Robotics

Abstract of Invited Speech
Recent advancements in robotics and artificial intelligence have brought about a new innovation, driven by the emergence of soft robotics and embedded intelligence. This talk will explore the exciting convergence of these two cutting-edge fields, showcasing how soft robots—known for their flexible, adaptable structures—are redefining applications across industries such as healthcare, agriculture, and beyond.
We will uncover the fundamental principles that make soft robotics unique, including the novel materials and sensors that enable these robots to interact safely and effectively with complex environments. In parallel, we will examine the crucial role of embedded intelligence: the integration of advanced AI algorithms directly into robotic systems, empowering them with real-time perception, autonomous decision-making, and enhanced versatility.
The talk will highlight the latest research trends, and the key challenges that researchers and engineers face when deploying soft robots endowed with intelligent capabilities. By gaining insight into these breakthroughs, we can better imagine a future in which soft, intelligent robots augment human abilities, enhance safety, and deliver smarter, more adaptive automated solutions.

Dr. Nabila A. Karim
Institut Sel Fuel,
University Kebangsaan Malaysia,Malaysia

Title of Invited Speech
To be announced

Abstract of Invited Speech
To be announced

Assoc. Prof. Ratchatin Chancharoen
Mechanical Engineering Department,
Robotics and AI Program Chulalongkorn University, Thailand

Title of Invited Speech
To be announced

Abstract of Invited Speech
To be announced

Assoc. Prof. Choi, Kyungwho
Mechanical Engineering Department,
Sungkyunkwan University, Korea

Title of Invited Speech
To be announced

Abstract of Invited Speech
To be announced