October 25, 2022
Thomas E Kazior, PhD, IEEE Fellow, Program Manager, DARPA Microsystems Technology Office (MTO)
Presentation Title: Heterogeneous Integration (HI): An Enabler For Next Generation Systems
Abstract: To perform increasingly diverse missions in increasingly crowded EM environments, future sensor and communication systems will require increased bandwidth and sensitivity and enhanced functionality per unit area. These needs are driving sensor arrays towards higher levels of integration of a diverse set of materials, devices and components across multiple domains. This includes 3D solutions, particularly at millimeter wave and THz frequencies. This talk will present an overview of the evolution of heterogenous integration programs at DARPA and potential paths forward, including 3DHI at the transistor level being explored under the DARPA Heterogeneous Heterostructures (H2) and related programs.
Biography: Dr. Thomas E. Kazior joined DARPA in July 2020 as a program manager in the Microsystems Technology Office (MTO). His research interests include semiconductor material and device design, fabrication and integration processes including 3D heterogeneous integration (HI) of silicon and compound semiconductor and other non-silicon devices for RF arrays, and microwave/millimeter-wave/sub-millimeter-wave devices for sensors and communications.
Kazior received his Doctor of Philosophy degree in material science and engineering, specializing in electronic materials, from the Massachusetts Institute of Technology. Prior to joining DARPA, he was a senior principal fellow at Raytheon Company's Integrated Defense Systems. Kazior has co-authored more than 100 publications, contributed and invited conference papers, and a book chapter on compound semiconductor and heterogeneous integration technology. He also has more than 20 patents in semiconductor fabrication technology. Kazior participated in the International Technology Roadmap for Semiconductors (ITRS), co-authoring the analog mixed signal chapter. He is an IEEE fellow.
Ivor Barber, AMD
Presentation Title: Integration Strategies in the Chiplet Era
Ivor Barber is Corporate Vice President of Packaging at AMD developing advanced package solutions with Chiplet Architecture in High Performance Computing, Graphics and Visualization Technologies.
With over 40 years experience in the field of Semiconductor Packaging Ivor has held engineering and engineering management positions at National Semiconductor, Fairchild, VLSI Technology, LSI Logic and Xilinx. Ivor is a frequent panelist and presenter in packaging forums with deep experience in leading edge packaging solutions and high density interconnect technologies. Ivor has 20 published US patents in Semiconductor Package Manufacturing and Package Design and is a board member of MEPTEC. Ivor has a Bachelor of Science Degree in Manufacturing Technology from Napier University, Scotland, UK.
Alan Duong, Meta Platforms
Presentation Title: Metaverse and the Future of Data Centers
Abstract: What is the Metaverse and how will it enable and inform the future of our Data Centers? The Metaverse is the next technological platform that will give people the power to connect and express themselves more naturally through an even more immersive – an embodied internet where you’re in the experience. In the Metaverse, you’ll be able to do almost anything you can imagine today as well as completely new experiences that don’t really fit how we think about computers, phones or even Data Centers. The future of this technological platform requires an innovative, sustainable, and thoughtful approach to data center designs and deployment at scale as well as adopting the advancement of future Metaverse technologies as a means to fully autonomous, self-reliant infrastructure developed to house next generation computers and not people. Join to learn more about how the vision for the Metaverse can and will influence the future of the Data Center.
Biography: Alan Duong is a Global Director of Data Centers Strategic Engineering & Design at Meta Platforms, where he leads a team of engineers and designers to research, create and develop new innovative technologies to improve reliability, safety, and efficiency of Meta's Data Center Infrastructure program. Alan earned his bachelor’s degree in Electrical Engineering from California Polytechnic State University, San Luis Obispo and spent his entire career developing engineering solutions and delivering infrastructure for the mission critical industry.
Mark S. Spector, Advanced Naval Platforms Division, Office of Naval Research
Presentation title and abstract forthcoming
Biography: Dr. Mark S. Spector is a Program Officer in the Advanced Naval Platforms Division at the Office of Naval Research where he manages programs in thermal science, metamaterials, and energy conversion. In addition, he sits on the Steering Committee of the Department of Defense Energy and Power Community of Interest and the NATO Applied Vehicle Technology Power and Propulsion Systems Technical Committee. Previously, he worked as a Research Physicist in the Center for Bio/Molecular Science and Engineering at the Naval Research Laboratory. He received his Ph.D. in Physics from the Massachusetts Institute of Technology and his A.B. in Physics and Applied Mathematics from University of California at Berkeley.
October 26, 2022
Dr. Philseok Kim, ARPA-E
Presentation Title: Pushing The Boundaries of Thermal Packaging for Enhanced Performance And Energy Efficiency
Abstract: With the advancement of cloud technologies and data centers, artificial intelligence, machine learning, and IoT-based data communications and operations, the power consumption of information and communication technology (ICT) has skyrocketed in recent years. Datacenters alone are estimated to consume 75 TWh/yr of electricity annually (approximately 2 percent of total US electricity consumption), and this consumption is expected to grow exponentially with exploding demand. However, the chip scaling efficiency (Moore's law) reached a tipping point in 2016, and conventional and incremental technological improvements to reduce electric power consumption have reached their limit, with the efficiency curve plateauing.
To make the next leap, transformative improvements in both performance and efficiency, as well as cost reductions, are required. Inefficiencies in complex, heterogenic integration of high powder density electronics result in inefficient power use and a large amount of waste heat, both of which must be managed for the reliable and efficient operation of ICT components and systems. Microscale thermomechanics and thermal management have become increasingly important as electronics have shrunk, but they are extremely difficult to understand and control due to the transient and spatially isolated nature of components or environments. Rather than dealing with thermal management in electronic devices as an afterthought, it is critical to employ a co-design approach early in the process to enable new designs. Quantum computing is rapidly approaching as a viable and unavoidable solution, with potential end use cases in a wide range of industries. However, for large-scale and cost-effective applications, better cooling solutions are critical. Transformational designs, materials, and manufacturing methods are needed, among other things, to reduce the cost and size of the associated cryogenics systems.
I will provide an overview of ARPA-E’s past and current program areas related to this problem, followed by a discussion of potential technology areas to push the boundaries of thermal materials and interfaces to enable the establishment of next generation electronics packaging.
Biography: Dr. Philseok Kim is a program director at ARPA-E with a focus on functional materials and composites, engineered surfaces and structures that enable and accelerate electrification and decarbonization of energy infrastructure with high energy-efficiency, resilience, and low carbon emissions. Prior to joining ARPA-E, Dr. Kim co-founded Adaptive Surface Technologies, Inc. leveraging ARPA-E-funded SLIPS projects and launching commercial products such as fouling-resistant and fuel-saving ship hull coatings. He also co-led SLIPS project at Harvard University to improve the energy efficiency of refrigerators by reducing frost formation and defrost time. Dr. Kim has 12 years of experience in petrochemicals, polymers, and coatings industry. Raised in South Korea, Dr. Kim received his B.S. and M.S. from Seoul National University then Ph.D. in chemistry at Georgia Institute of Technology. Dr. Kim has published over 40 papers in high-impact, peer-reviewed journals and holds about 40 issued patents in surface functionalization, materials for organic field-effect transistors, adaptive optical materials, and slippery surfaces.
October 27, 2022
Abhinav Saxena, GE Research, USA
Presentation Title: Use of AI and ML in Improving Industrial Reliability towards Achieving Operational Autonomy – Successes and Challenges
Abstract: Predictive Maintenance (PM) is becoming ubiquitous for improving availability and reliability along with reducing O&M costs in industrial systems. Despite significant research and development investment in the last decade most deployed solutions still tend to be piecemeal (component or failure mode specific) point solutions and generally lack trust with respect to automated decision making. Full end-to-end deployment with system-wide coverage and autonomy still remains an elusive goal in industrial setting. This is primarily due to high cost and limited scalability of conventional modeling approaches for underlying complex systems and processes in large fleets. Specifically, capabilities to safe-guard against unknown-unknowns, lack of explainability and trust tend to be key bottlenecks. Given these systems are heavily instrumented generating large volumes of high-speed data and compute costs continue to go down, recent advancements in data-driven methods using machine learning (ML) and artificial intelligence (AI) have shown promise in a number of areas that previously led to valley of death between PM technology and commercialization. GE’s Digital Twin technology for Predictive Maintenance is leveraging AI to bridge a number of such critical gaps that were otherwise very challenging to tackle through conventional methods. This session will enumerate key challenges in enabling system-wide predictive maintenance and how AI is being used to overcome these. Specifically, a causal deep learning-based approach will be described that provides a causal graph of inter-variable relationships allowing validation of deep learning model with domain experts. Further, by providing causal factors for identified anomalies root cause analysis can be facilitated for alert disposition in efficient manner at the fleet level. We will also describe our approach towards competency awareness of AI models, which aims to solve uncertainty management and trust for industrial applications of AI. Various applications and use-cases will be shared to show effectiveness of AI and ML using both structured and unstructured data in the context of intelligent PM.