Invited Speakers

Application Area 1

Contribution to solving social problems through MOEMS technology that enables equipment miniaturization

Toshihisa Atsumi

Manager, Hamamatsu Photonics K.K

Abstract

MOEMS (Micro Opto Electro Mechanical Systems) technology, which adds optical functions to MEMS, is effective for miniaturization and mass production of spectrometers and scanning mirrors. Devices that were previously used in laboratories and hospital diagnostic rooms have been miniaturized and made into portable devices, etc. They are now being used in places close to our daily lives. On the other hand, many developed countries are facing the challenges of declining working-age populations, global warming, and plastic waste. In this talk, I will present how MOEMS technology can contribute to these social issues with specific examples.

Application Area 4

More than Moore Emerging Technologies for the Sensing Needs in EV

Eloi Marigo Ferrer

Silterra (M) Sdn Bhd

Abstract

EV, IoT and industry 4.0 open new market opportunities in the semiconductor field. The potential is high but so are the expectations to quickly fulfil the stringent specifications and requirements for tomorrow’s products. The customer focus is shifting into embedding more and more capabilities into their devices in order to develop smart products. Several challenges must be faced in each of the stages of the MEMS and sensors development: from the conceptual design employing unconventional materials and unique processes to the characterization and reliability assessment of complex structures. In this talk we will provide an overview to the journey from a conventional pure-play CMOS foundry to become a reference partner in more than Moore technologies with special emphasis into MEMS, sensors and its monolithic integration with CMOS devices.

Application Area 5

The trend of circularly mitigating energy consumption, pollutants and wastes from the prospect of Fab Facility

Luh-Maan Chang

Professor Emeritus, National Taiwan University & Purdue University

Abstract

The innovative use of semiconductor chips continuously enhances applied technology, scientific knowledge and human life. To facilitate its manufacturing, fabrication plant (fab) is a part of and prerequisite to the manufacturing. In the fab, the latest process of chips manufacturing consumes huge energy of electricity and water, and generates a lot of pollutants and wastes.

The speech will begin with a brief introduction of Green Manufacturing and its needed Fab Facility Systems following with their intertwined relationship and issues. Then, TSMC’s mega-fab will be used to exemplify the strategic methods of mitigating energy consumption, contaminants, wastes and their technic trends

Application Area 6

Development of MBS-LAB: an Automated Bio-Experiment System for Microgravity Conditions in Low Earth Orbit using Our Semiconductor-Based Microscopic Observation Device

Masano Nakayama

IDDK Co., Ltd.

Abstract

Numerous bio-experiments have been conducted on the International Space Station (ISS). As private sector involvement in space grows, the importance of space-based bio-experiments continues to increase. However, the upcoming retirement of the ISS, high costs, and limited opportunities remain significant challenges. To address these issues, we are developing a low-cost bio-experiment platform using low Earth orbit satellites. Specifically, we have developed MBS-LAB, a fully automated bio-experiment system for microgravity environments, integrated with a semiconductor-based Micro Imaging Device (MID). Furthermore, MBS-LAB employs a proprietary scripting system called “RECIPE” for device operation, allowing experimenters to implement experimental protocols on the system without the need to directly edit control programs. In this talk, I will introduce the details of both MID and MBS-LAB.

Development of an AI-Powered Robotic System for Vineyard Grape Harvesting

Liangliang Yang

Associate Professor, Kitami Institute of Technology

Abstract

This study presents the development of an AI-powered robotic system designed for the autonomous harvesting of vineyard grapes. The system integrates computer vision, deep learning-based fruit detection, and robotic manipulation to identify and harvest ripe grape clusters with precision. Utilizing RGB and infrared imaging, the robot can operate under varying lighting and occlusion conditions commonly found in vineyards. An EV-type wheel-drive vehicle enables navigation across uneven terrain and slopes using RTK-GNSS. Field trials demonstrate improved harvesting efficiency, reduced labor requirements. The proposed system offers a promising solution to labor shortages and enhances the automation of viticulture operations.