IEEE INEC, which stands for IEEE International Nanoelectronics Conference, was initiated in 2006 when the field of nanoelectronics was emerging and will host its 2025 conference in Taipei on Jan 3-6.
Dr. Cher Ming Tan, the General Chair of IEEE INEC 2025, was the founding chair of this conference while he was Director of SIMTech-NTU Reliability Laboratory and a Senior Scientist at SIMTech in Singapore. He spoke with DIGITIMES Asia regarding the theme, "Interplay between AI and Nanoelectronics." The conference is calling for papers with the deadline set at the end of August 2024.
Q: Could you give a brief introduction to the conference, and why set the theme to talk about the interplay between nanoelectronics and AI?
Tan: This event is a key initiative of the IEEE Nanotechnology Chapter in Singapore, created to provide a platform for global collaboration in nanotechnology. The conference has been held in various countries including Malaysia, Japan, Singapore, Taiwan, China, and Hong Kong, attracting numerous participants involved in nanoelectronics research.
The primary goal of this conference is to create a starting point for countries worldwide to engage in nanoelectronics simultaneously, fostering international cooperation and advancement. Despite originating in Asia, the conference draws significant participation from Europe, America, and other regions.
In recent years, with the rapid advancement of Artificial Intelligence (AI), I decided to focus the theme of the 2025 conference on the interplay between nanoelectronics and AI. This theme highlights the crucial role of nanoelectronics in making AI technologies viable. Historically, AI theory has existed for decades, but practical implementation was hindered by inadequate hardware. The evolution of nanoelectronics has enabled the development of powerful, compact processing chips and sensors, which are essential for AI's data processing and sensory capabilities.
Nanoelectronics has made AI technology more feasible and cost-effective, integrating it into daily life. Conversely, AI's ability to process and organize vast amounts of data benefits nanoelectronics research, which involves extensive data from materials and device studies. Thus, the synergy between nanoelectronics and AI accelerates advancements in both fields. This mutual enhancement is the essence of the chosen theme, underscoring the importance of nanoelectronics in the impressive advancements of AI and vice versa.
Q: Any new applications and new research directions that you have been discovering through the existence of AI?
Tan: The advancements in computational power, partly due to the development of quantum computing, have significantly enhanced our ability to perform complex calculations. Quantum computing, enabled by nanotechnology, has revolutionized computation by increasing processing power. AI now leverages this platform to synthesize new materials, a task previously impossible due to computational limitations.
With the immense power of quantum computing, AI can combine different elements from the periodic table to create materials with unique or desirable properties. This capability allows for the exploration and synthesis of materials with tailored characteristics, expanding the range of usable materials. By using quantum mechanics-based modeling tools, we can predict the properties of these new materials, opening up numerous possibilities in material science.
The upcoming conference will include topics on these advancements, highlighting how AI and quantum computing are pushing the boundaries of material discovery and application.
Q: So not only AI, but quantum computing is also becoming one of the tools you have been leveraging in your research. But in Taiwan, there's not enough attention on quantum computing. So this conference may open the eyes of the researchers here.
Tan: Yes indeed. This conference will feature a variety of prominent plenary and keynote speakers who are distinguished researchers in their fields. The list of speakers includes both academic and industrial experts. From the industry side, we have the General Manager of Infineon China, who will discuss sensors, which are essential components for AI. Additionally, a chief from Microsoft's Cloud Computing Data Center will address the importance of cloud computing in AI, highlighting how AI relies on robust cloud infrastructure.
The conference will also host numerous academic researchers focusing on nanotechnology, device fabrication, material synthesis, and related areas. These speakers will provide insights into the latest advancements and future directions in nanoelectronics and AI. By bringing together these diverse experts, the conference aims to foster new perspectives and collaborations within the scientific community.
Holding the conference in Taiwan, a leader in electronics and nanoelectronics, is strategic. It will provide attendees with a comprehensive view of the current state and future potential of these technologies, enabling them to align their research and development efforts with the latest trends and innovations.
We have invited speakers from Singapore, Europe, and the United States. I hope more participants from Taiwan will share their work on the international platform. With this international conference, it will be a good chance for researchers to present their papers here. They not only can learn from experts, but local researchers can also present their work to showcase the capability of Taiwan in this area.
Q: As a prominent reliability research expert in the world, how do you see generative AI aiding the research of your field? How have the academics and the industry been adapting this new tool?
Tan: I attempted to use GenAI to improve reliability analysis, but the results were disappointing. Here's why:
Firstly, GenAI relies on existing information available on the internet. Unfortunately, in the field of reliability, there is a significant amount of incorrect information online. It struggles to discern accurate data from inaccuracies. This issue is exacerbated because our understanding of product failure and reliability is constantly evolving. What was once considered correct is often updated as new discoveries are made, but AI can't distinguish between outdated and current information. Consequently, it often aggregates incorrect data, leading to more inaccuracies than reliable insights.
Secondly, although AI uses statistical methods, it is notably weak in statistical analysis, which is critical for reliability studies. AI's limitations in handling statistical nuances mean it cannot provide reliable results in this area.
Thirdly, with the advent of nano-devices, new failure mechanisms are emerging. These novel issues are not well-represented in existing data, so AI cannot predict or analyze these new failure modes accurately. This is because AI's knowledge base is inherently historical and lacks the foresight to handle cutting-edge developments.
In practice, I now use AI to gather all relevant information on a specific reliability issue. Then, using my expertise, I scrutinize this data to identify what is valid and what is not. This combination of AI for data collection and human expertise for analysis is essential. Relying solely on AI without expert validation can perpetuate outdated knowledge rather than advance our understanding.
Overall, while AI can assist in collecting data, expert domain knowledge is crucial to interpret and validate this information, ensuring the advancement of accurate and current insights in reliability analysis.
Q: People may forget that it was the old information that generative AI gathered from the internet instead of helping to find the correct answers or helping to explore new possibilities. Are there other insights you would like to share from your research or nanotechnology?
Tan: Nanotechnology is advancing at an astonishing pace, revealing new physical phenomena and opening up areas we never anticipated. For instance, the miniaturization of transistors has led to a tremendous increase in the number of transistors in integrated circuits and significantly increased power consumption, leading to overheating issues in chips. To address this, researchers are developing 2D transistors, which reduce power requirements and heat generation. Some of this groundbreaking work will be presented at the upcoming conference.
Additionally, nanotechnology has enabled us to increase the surface area of materials, enhancing their reactivity. For example, rice husks, often discarded as agricultural waste, can be modified using nanotechnology to purify water, showcasing an unexpected and valuable application. These smaller dimensions and increased exposed surfaces reveal more prominent surface phenomena than those seen in bulk materials.
In display technology, quantum dots are a testament to nanotechnology's impact. These tiny particles are used in quantum dot televisions to produce brighter, more uniform images while consuming less power. This advancement highlights nanotechnology's potential to revolutionize electronics, making devices faster and more energy-efficient.
The integration of AI with nanotechnology amplifies these possibilities. AI's data analytics capabilities combined with nanotechnology can drive innovations that were previously unimaginable. This synergy has the potential to bring about significant technological advancements, though it also carries risks. In the right hands, these technologies can profoundly benefit humanity, but they could be dangerous if misused.
Overall, the convergence of AI and nanotechnology promises to revolutionize technology, offering a nearly infinite array of possibilities, from more efficient electronics to novel material applications.
Editor's note: Cher Ming Tan, now the Director of the Center for Reliability Sciences and Technologies, and professor at the Department of Electronic Engineering, Chang Gung University in Taiwan, was a Tenure Associate Professor at Nanyang Technological University, Singapore 1996-2014. He was named Top 0.15% of Scientists (across all science disciplines) in 2021, 2022, and 2023, as well as the USA Top 2% Scientists Lifetime Achievement Award 2022 and 2023 by Stanford University. He is a Fellow of the Institute of Engineers, Singapore Fellow of the Singapore Quality Institute, and Fellow of the Institute of Association of Advanced Materials. He has won the Sweden IAAM Scientist Medal and has been an editor for several academic periodicals including IEEE TDMR, MDPI, and Scientific Report, Nature. He serves as Sweden IEEE Electron Devices Distinguish Lecturer, Chair of IEEE IRPS BEOL 2025, and is a founding member of the Taiwan Reliability Technology Association.
Credit: Cher Ming Tan
