IEEE Alaska Section

Two-Dimensional Semiconductors for Low-Power Logic and Memory Devices Deep Jariwala Department of Electrical and Systems Engineering University of Pennsylvania, USA Abstract: Silicon has been the dominant material for electronic computing for decades and very likely will stay dominant for the foreseeable future. However, it is well-known that Moore’s law that propelled Silicon into this dominant position is long dead. Therefore, a fervent search for (i) new semiconductors that could directly replace silicon or (ii) new architectures with novel materials/devices added onto silicon or (iii) new physics/state-variables or a combination of above has been the subject of much of the electronic materials and devices research of the past 2 decades. The above problem is further complicated by the changing paradigm of computing from arithmetic centric to data centric in the age of billions of internet-connected devices and artificial intelligence as well as the ubiquity of computing in ever more challenging environments. Therefore, there is a pressing need for complementing and supplementing Silicon to operate with greater efficiency, speed and handle greater amounts of data. This is further necessary since a completely novel and paradigm changing computing platform (e.g. all optical computing or quantum computing) remains out of reach for now. The above is however not possible without fundamental innovation in new electronic materials and devices. Therefore, in this talk, I will try to make the case of how novel layered two-dimensional (2D) chalcogenide materials and three-dimensional (3D) nitride materials might present interesting avenues to overcome some of the limitations being faced by Silicon hardware. I will also highlight ongoing work and opportunities to extend the application of III nitride ferroelectric materials into extreme environments electronics. Then, on the optical and photonic materials side I will first make the case for van der Waals bonded semiconductors which exhibit strong excitonic resonances and large optical dielectric constants as compared to bulk 3D semiconductors. First, I will focus on the subject of strong light-matter coupling in excitonic 2D semiconductors, namely chalcogenides of Mo and W. Visible spectrum band-gaps with strong excitonic absorption makes transition metal dichalcogenides (TMDCs) of molybdenum and tungsten as attractive candidates for investigating strong light-matter interaction formation of hybrid states. I will then extend the analogy to hybrid 2D materials and 1D carbon nanotubes. Bio: Deep Jariwala is an Associate Professor and the Peter & Susanne Armstrong Distinguished Scholar in the Electrical and Systems Engineering as well as Materials Science and Engineering at the University of Pennsylvania (Penn). Deep completed his undergraduate degree in Metallurgical Engineering from the Indian Institute of Technology in Varanasi and his Ph.D. in Materials Science and Engineering at Northwestern University. Deep was a Resnick Prize Postdoctoral Fellow at Caltech before joining Penn to start his own research group. His research interests broadly lie at the intersection of new materials, surface science and solid-state devices for computing, opto-electronics and energy harvesting applications in addition to the development of correlated and functional imaging techniques. Deep’s research has been widely recognized with several awards from professional societies, funding bodies, industries as well as private foundations, the most notable ones being the Optica Adolph Lomb Medal, the Bell Labs Prize, the AVS Peter Mark Memorial Award, IEEE Photonics Society Young Investigator Award, IEEE Nanotechnology Council Young Investigator Award, IUPAP Early Career Scientist Prize in Semiconductors and the Alfred P. Sloan Fellowship. He has published over 150 journal papers with more than 21000 citations and holds several patents. He serves as the Associate Editor for ACS Nano Letters and has been appointed as a Distinguished Lecturer for the IEEE Nanotechnology Council for 2025. Place: 230A Davis Hall, University at Buffalo, North Campus, Buffalo, NY 14260 Date and time: March 28th, Friday 2025, 2pm EST Host: Huamin Li ([email protected]) on behalf of the IEEE Buffalo Section Co-sponsored by: University at Buffalo 230A Davis Hall, Buffalo, New York, United States, 14260

Building and managing hardware infrastructure in the Cloud plays a critical role in the development of AI training models. It provides the essential resources needed to handle the complexity, scale, and computational demands of these workloads. In this session, we will provide an overview on the hardware infrastructure that powers AI training models and the challenges that arise within the Cloud environment. The discussion will begin from building the infrastructure to generating the model, and finally, to the creation of business value. The complimentary series on AI will be recorded for on demand access. Please register (to attend or to get link to recorded version) ================ Overall series The IEEE AI Coalition has organized a series of live webinars centered around the various topics of AI. Each webinar is composed of expert speakers that may cover both the technical aspects of AI as well as socio-economic, ethical, legal, and other implications with AI. The IEEE AI Coalition is a collective of representatives from across IEEE who have come together with a shared purpose: to consolidate all of IEEE’s AI content into a cohesive platform and foster a robust network for AI-related resources. Our mission encompasses three key objectives: Classify and Index AI Work: We diligently organize and categorize AI-related work from across IEEE, ensuring that it is accessible in one centralized area. By doing so, we aim to bridge any gaps and facilitate seamless exploration of AI research, applications, and insights. Enhancing IEEE Services with AI: We explore how artificial intelligence can enhance the services provided by IEEE. Whether it’s optimizing processes, improving member experiences, or advancing technical publications, we strive to leverage AI effectively. Long-Term Vision: The AI Coalition is committed to developing a forward-thinking, long-term vision for the integration of AI within IEEE. We envision a future where AI-driven innovations empower our members, drive societal progress, and shape the technological landscape. Join us on this transformative journey as we unlock the potential of AI within IEEE and the world! Co-sponsored by: IEEE Future Tech Forum series on AI. Speaker(s): Christine, Emma, Sam Virtual: https://events.vtools.ieee.org/m/477913

Abstract – There is a significant range of physical phenomena—from nonlinear elasticity, to symmetry, noise, topology, and disorder — that are rarely utilized in traditional computing paradigms. Yet these phenomena can unlock new efficiencies, by directly processing signals in their natural domain, and by bypassing the traditional abstraction stack associated with digital CMOS technology. However, building physical computers is challenging. Information processing tasks generally involve complex input-output relations, thus requiring designs that are highly expressive; and for these designs, the relation between function and structure is nontrivial, complicating the simulation, design, and fabrication of devices. In my talk, I will illustrate our journey towards using metamaterials for physical computing, with two recent examples. First, I will talk about our results in passive speech recognition, where we leverage a phononic metamaterial to implement wake-up-word detection with zero standby power consumption. Second, I will discuss our ongoing work in self-learning materials, that autonomously adapt to improve their performance—driven by their ability to form long-term memories in response to examples and external feedback. Co-sponsored by: Advanced Science Research Center - the Graduate Center - City University of New York Speaker(s): Marc Serra-Garcia Room: Auditorium, Bldg: Advanced Science Research Center CUNY, 85 St. Nicholas Terrace 2.325, New York, New York, United States, NY 10031, Virtual: https://events.vtools.ieee.org/m/473401