🇺🇸 미국 · "OVERCOME" · 총 18건
필터 보기현재 지수
48.8
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 10,775건을 분석한 결과, 뉴스 심리지수는 48.8(균형)입니다. 긍정 1,082건(10.0%)·중립 7,691건(71.4%)·부정 2,002건(18.6%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 22.5(보수 경향)입니다.
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The EPICS (Engineering Projects in Community Service) in IEEE program, administered by IEEE Educational Activities, has launched the Excellent EPICS in IEEE Contributor Awards. The recognitions honor the program’s outstanding students and faculty volunteers in Excellent Team Leader and Excellent Faculty Advisor categories. The awards recognize individuals whose leadership, mentorship, and commitment have meaningfully advanced the impact of EPICS projects. Candidates must demonstrate clear, measurable contributions that elevate both the student experience and the outcomes delivered to community partners. Reviewers also consider other awards, publications, presentations, and professional achievements that reinforce the nominee’s credibility and leadership. Recipients must demonstrate outstanding project management and documentation, strong mentoring and collaboration, and high-quality outcomes. Here are this year’s recipients. Team Leader Award Surattana Kakay is a computer engineering student at Rajamangala University of Technology Thanyaburi (RMUTT), located in IEEE Region 10 (Asia Pacific). Kakay, an IEEE student member, was honored for guiding her team in the design, development, and implementation of the Automatic Water Level Control System project, which aids rice farmers in Thailand. As the team leader, Kakay played a pivotal role in transforming the student initiative into an operational, community‑centered solution. Her inspiration was purpose-driven, she says. “My motivation was to apply engineering to real agricultural challenges, like water scarcity and climate change,” she says. “I wanted to bridge advanced technology with the tangible needs of local farmers.” She managed the project end to end—coordinating workflow, assigning tasks based on team members’ strengths, and ensuring each phase of development aligned with the technical road map she created. She served as the primary liaison between the student team, the Pathum Thani Rice Research Center, and farmers to make sure the system was practical and user‑friendly, and that it addressed community needs. “Watching students grow as they design solutions that improve lives has been both inspiring and deeply humbling.” —Elizabeth Vidal-Duarte Under her leadership, the team developed a low‑cost IoT‑based alternate wetting and drying (AWD) system that lets farmers remotely monitor and control water levels in rice paddies using smartphones. Kakay oversaw the integration of noncontact laser time‑of‑flight sensors to withstand harsh field conditions, and she championed the use of long-range technology connected to a free community Wi‑Fi network to eliminate Internet service fees. The results were transformative, Kakay says. “Our AWD system reduces water consumption by 63 percent and methane emissions by 7 percent annually,” she says. “Turning an academic assignment into a real‑world solution that delivers measurable, sustainable results has been incredibly meaningful.” Her achievements advanced sustainability for Thailand’s most water‑intensive crop while demonstrating the potential of accessible engineering solutions. Beyond technical innovation, Kakay cultivated a culture of learning, continuity, and empowerment within her team. She introduced a mentorship framework to support future student cohorts. She and her team produced academic papers, visual media, and presentations to communicate the project’s value to scientific audiences as well as the general public. “Surattana Kakay is a pivotal figure in turning innovation into reality and delivering tangible benefits to the community,” says IEEE Member Thanasin Bunnam, her faculty advisor and an assistant professor at RMUTT. Kakay’s leadership journey became a personal milestone, she says: “Leading this project transformed me from a student into a team leader. As a female engineer, it empowered me to advocate for women in engineering and show that gender is no barrier to technical excellence.” Through her guidance, the AWD project evolved from a classroom assignment into a solution that illustrates IEEE’s mission of advancing technology for humanity. Faculty Advisor Awards Navid Shaghaghi, a lecturer and researcher at Santa Clara University, in California, was recognized for his dedication to integrating service learning into engineering education and fostering student innovation that benefits underserved communities in IEEE Region 6 (Western USA). During his more than six years of engagement with EPICS in IEEE, Shaghaghi, an IEEE senior member, has demonstrated exceptional leadership in advancing sustainable, human‑centered engineering through the long‑running Hydration Automation (HA) project and the HiveSpy initiative. They are part of Santa Clara University’s Frugal Innovation Hub and EPIC Research Laboratory. Since 2019, Shaghaghi has served as principal investigator for the HA project, guiding its evolution from prototype to a robust, field‑tested irrigation automation system that supports small ranches and community farms in California. The HA project is a low‑cost system that helps reduce water waste by monitoring soil moisture and automating watering. By combining ultrasonic tank sensing, soil sensors, and ongoing technical support, the project improves efficiency, lowers operational costs, and promotes more sustainable urban agriculture. Under Shaghaghi’s guidance, more than 30 undergraduate and graduate students have gained hands-on experience in IoT development, field deployment, testing, and client collaboration. His commitment to frugal innovation and human‑centric design has resulted in solutions that are minimalist, affordable, sustainable, portable, and rugged—often challenging conventional approaches to agricultural technology. “Turning an academic assignment into a real‑world solution that delivers measurable, sustainable results has been incredibly meaningful.” —Surattana Kakay The HA project has produced new research publications and earned recognition, including a third-place finish by Shaghaghi’s graduate students at this year’s IEEE Rising Stars Project Showcase. During the annual event, students and young professionals present their technical innovations to industry leaders and peers. The HiveSpy project is a low‑cost, frame‑level IoT monitoring system that helps beekeepers automate labor‑intensive tasks and prevent hive swarming by tracking production yield in real time. By collecting frame‑weight data and generating optimized harvest schedules, the system reduces manual workload while improving the hive’s health and boosting honey output. Shaghaghi says his mentorship has been shaped by the realities of student turnover, a challenge he embraces with optimism and adaptability. “The transient nature of student teams is a challenge but one you must embrace, bear‑hug style,” he says. “By energizing your student community and welcoming new contributors, you’ll be amazed by the brilliant solutions they bring.” His philosophy has allowed him to cultivate a thriving pipeline of student innovators, he says, and he has strengthened his own professional practice as well. “I’ve been mentoring EPICS in IEEE students since 2019,” he says. “It has taught me resilience and how to operate on a tight budget while still delivering real‑world results.” Beyond the technical achievements, Shaghaghi’s work reflects a commitment to humanitarian technology and service learning. As the founder and director of the EPIC (Ethical, Pragmatic, and Intelligent Computer) lab, he has built a diverse, interdisciplinary community dedicated to innovation for the benefit of humanity. For him, he says, the EPICS in IEEE award carries profound meaning: “Receiving this award validates my deepest conviction in humanitarian technology research and strengthens my commitment to service‑learning education.” His students echo those sentiments. One team member said “Professor Shaghaghi is an engine of progress who keeps forging ahead.” Through his leadership, Shaghaghi has created an enduring model of mentorship, innovation, and community partnership that is helping to shape the next generation of socially responsible engineers. Elizabeth Vidal-Duarte is celebrated for her impactful mentorship and leadership in expanding EPICS in IEEE engagement across Peru and IEEE Region 9 (Latin America and Caribbean). Vidal-Duarte, a research professor at San Agustin National University Arequipa, in Peru, is a faculty advisor and technical mentor for two EPICS in IEEE projects. She encouraged students to apply to the EPICS program, helped them identify community needs, and supported them in crafting proposals grounded in service‑learning principles. Under her leadership, the students developed a functional soft robotic glove used at Clínica San Juan de Dios to help patients improve their fine-motor skills. The clinic’s therapists use the device to measure the range of motion of joints at the beginning and end of each patient’s therapy session to improve their assessments. Compared with traditional manual measurements using a goniometer, the glove significantly reduces evaluation time and enables digitally recorded data, improving clinical efficiency and decision-making. The second project is an emotion‑recognition system for people with visual impairment. The AI‑powered wearable helps recognize a person’s emotions through real‑time facial‑expression detection and haptic feedback. The project has resulted in the “Emotion-Aware Assistive System With Wearable Haptic Feedback for Visual Impairment” research paper, which is to be presented at the IEEE International Symposium on Computer-Based Medical Systems, to be held from 3 to 5 June in Limassol, Cyprus. Vidal-Duarte’s mentorship extends beyond the classroom. She visits rehabilitation centers and clinics to find people with visual impairments to ensure that the technologies she is helping to develop meet their needs. “EPICS in IEEE has moved me beyond teaching concepts to truly living engineering as a tool for human impact,” Vidal-Duarte says. “Watching students grow as they design solutions that improve lives has been both inspiring and deeply humbling.” Throughout the development of both projects, Vidal-Duarte provided sustained technical and organizational guidance, helping students define requirements, structure work plans, and overcome challenges in prototyping, testing, and validation. Reflecting on the broader impact of EPICS, she says the program has given her “more than methodologies and tools—it has given me perspective, purpose, and a global community that constantly challenges me to grow as a mentor and as a human being.” Her mentorship fostered not only technical excellence but also empathy, ethical awareness, and professional maturity among her students, she says. She guided them in preparing articles for submission to IEEE conferences, interdisciplinary collaboration, and hands-on fieldwork that bridged theory and real‑world constraints. “Her constant support, her belief in each student’s potential, and her commitment to developing leaders who make a difference define [her] as a faculty advisor,” says Valentina Chabilla, an EPICS in IEEE student team member. The EPICS recognition reflects her passion for teaching, her dedication to the community, and her impact on projects and students. Her commitment to accessible, sustainable innovation strengthened partnerships between the university and community groups, benefiting underserved populations. “Receiving this award is both an honor and a responsibility,” she says. “It reminds me of the real impact engineering can have on people’s lives and strengthens my commitment to guiding students in creating meaningful change.” Her leadership continues to inspire students to view engineering not just as a discipline but also as a powerful force for inclusion, dignity, and social impact. Advancing the mission The Excellent Contributor Award recipients exemplify the best of EPICS in IEEE. Through their leadership, they have strengthened the bridge between engineering education and community service, inspiring students to use their skills to create sustainable, real‑world impacts. As EPICS continues to expand its global reach, the contributions of Kakay, Shaghaghi, and Vidal-Duarte serve as powerful reminders of what is possible when educators, volunteers, and students work together to improve the lives of others through engineering.
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Editor’s note: If you’d like to pinpoint the instant when the world entered the nuclear age, 5:29:45 a.m. Mountain War Time on 16 July 1945, is an excellent choice. That was the moment when human beings first unleashed the power of the nucleus in an immense, blinding ball of fire above a gloomy stretch of desert in the Jornada del Muerto basin in New Mexico. Emily Seyl’s Trinity: An Illustrated History of the World’s First Atomic Test (The University of Chicago Press) offers hundreds of startlingly vivid photographs of the Manhattan Project that emerged from a 20-year restoration effort. This excerpt and the accompanying photos record the massive effort to capture the awesome detonation of “the Gadget.” aspect_ratioReprinted with permission from Trinity: An Illustrated History of the World’s First Atomic Test by Emily Seyl with contributions by Alan B. Carr, published by The University of Chicago Press. © 2026 by The University of Chicago. All rights reserved. In the North 10,000 photography bunker, Berlyn Brixner was listening to the countdown on a loudspeaker, his head inside a turret loaded with cameras and film. He was one of the only people instructed to look toward the blast—through his welder’s glasses—ready to follow the path of the fireball as it launched into the sky. The two Mitchell movie cameras at his station would deliver the best footage to come of the Trinity test, used by Los Alamos scientists to make some of the first measurements of the effects of a nuclear explosion. Related: New Trinity Book Uncovers Images of the First Atomic Test When the detonators fired, the cameras captured what Brixner could not have seen—the very first light of a violent, silent sea of energy unfurling into the basin. As 32 blocks of high explosives erupted all together, their incredible force surged inward toward the sleeping plutonium core, compressing the dense sphere of metal instantaneously from all sides and bringing its atoms impossibly close together. A carefully timed burst of neutrons sowed momentary, uncontrolled chaos, and then, as quickly as it began, the fission chain reaction ended. Footage from a high-speed Fastax camera in Brixner’s bunker, shot through a thick glass porthole, shows a translucent orb bursting through the darkness less than a hundredth of a second after detonation, as a rush of heat, light, and matter blew apart the Gadget. When the brightness faded enough for witnesses to make out ground zero, they saw a wall of dust rise up around a brilliant, shape-shifting, multicolored ball of flames—forming a fiery cloud that shot into the sky atop a twisting stream of debris. The camera footage tells a story no less dramatic but hundreds of times more intricate, preserving the moment for scientists to return to again and again to measure and describe the behavior of the fireball and other visible effects with exacting detail. On balance, the photography effort was a huge success, despite only 11 of the 52 cameras producing satisfactory images. By arranging those cameras at intentionally staggered distances, complementary angles, and with a broad spectrum of frame rates and focal lengths, the Spectrographic and Photographic Measurements Group was able to piece together a remarkably complete picture of their subject. On 12 July 1945, Herbert Lehr, a U.S. Army sergeant and electrical engineer assigned to Los Alamos, delivered the plutonium core to the McDonald ranch house, where the bomb was assembled. Los Alamos National Laboratory According to the group’s leader, Julian Mack, the more than 100,000 frames that were captured still “give no idea of the brightness, or of time and space scales.” Mack attributed fortune, as much as foresight, to the photographic record that was made, especially during the earliest phase of the blast. Indeed, the explosion was several times more powerful than predicted, and the intensity of its effects overwhelmed many of the cameras and diagnostic instruments. The human observers were similarly overcome. “The shot was truly awe-inspiring,” said Norris Bradbury, the physicist who would succeed Robert Oppenheimer as director of Los Alamos. “Most experiences in life can be comprehended by prior experiences, but the atom bomb did not fit into any preconception possessed by anybody. The most startling feature was the intense light.” Norris Bradbury, the physicist responsible for the final assembly of the Gadget, stands next to the partially assembled bomb at the top of the shot tower. The cables on the outside of the bomb would transmit the signals to trigger the synchronized detonations of conventional explosives, which would then create the inward-directed shock wave that would compress the bomb’s plutonium core. Bradbury would go on to succeed Robert Oppenheimer as director of Los Alamos on 17 October 1945.Los Alamos National Laboratory It is a common sentiment that words and even pictures pale in comparison to the experience of the explosion. Even so, soldiers, scientists, and many other witnesses have added their firsthand accounts—often absorbing and poetic—to complement the trove of hard data collected during the test shot. They describe an intense and blinding brightness that filled the basin with daytime; an ominous, darkening cloud rearing its head in eerie silence; the wait for the invisible wave rushing out from the heart of the Gadget; and the mighty roar that arrived at last, in a thunder, and seemed never to leave. Physicist Isidor Isaac Rabi, watching from 20 miles away, remembered, “It blasted; it pounced; it bored its way right through you.” James Chadwick, head of the British contingent of scientists who joined the Manhattan Project, later said, “Although I had lived through this moment in my imagination many times during the past few years and everything happened almost as I had pictured it, the reality was shattering.” The blast, captured with an assortment of high-speed and motion-picture cameras, shows the fireball expanding between 25 milliseconds and 60 seconds, by which time the mushroom cloud is over 3 kilometers high.Los Alamos National Laboratory And physicist George Kistiakowsky found himself certain that “at the end of the world—in the last millisecond of the Earth’s existence—the last human will see what we saw.”
This sponsored article is brought to you by Applied Materials. At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs — such as those achieved under the Human Genome Project — required a new operating paradigm: Concentrate the world’s best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and siloed innovation simply cannot keep pace. Today’s AI era is creating an engineering race with similar demands. Every company is pushing to deliver higher-performance AI systems, faster. But performance is no longer defined by compute alone. AI workloads are increasingly dominated by the movement of data: In many cases, moving bits consumes as much — or more — energy than compute itself. As a result, reducing energy per bit can extend system‑level performance alongside gains in peak compute. The path to energy‑efficient AI therefore runs through system‑level engineering, spanning three tightly interconnected domains: Logic, where performance per watt depends on efficient transistor switching, low‑loss power, and signal delivery through dense wiring stacks. Memory, where surging bandwidth and capacity demands expose the memory wall, with processor capability advancing faster than memory access. Advanced packaging, where 3D integration, chiplet architectures, and high‑density interconnects bring compute and memory closer together — enabling system designs monolithic scaling can no longer sustain. These domains can no longer be optimized independently. Gains in logic efficiency stall without sufficient memory bandwidth. Advances in memory bandwidth fall short if packaging cannot deliver proximity within thermal and mechanical constraints. Packaging, in turn, is constrained by the precision of both front‑end device fabrication and back‑end integration processes. In the angstrom era, the hardest problems arise at the boundaries — between compute and memory in the package, front‑end and back‑end integration, and the tightly coupled process steps needed for precise 3D fabrication. And it is precisely this boundary‑driven complexity where the traditional innovation model breaks down. The Traditional R&D Workflow Is Too Slow for Angstrom‑Era AI For decades, the semiconductor industry’s R&D model has resembled a relay race. Capabilities are developed in one part of the ecosystem, handed off downstream through integration and manufacturing, evaluated by chip and system designers, and only then fed back for the next iteration. That model worked when progress was dominated by relatively modular steps that could be scaled independently and simply dropped into the manufacturing flow. But the AI timeline has upended these rules. At angstrom‑scale dimensions, the physics enforces inescapable coupling across the entire stack: materials choices shape integration schemes; integration defines design rules; design rules dictate power delivery; wiring sets thermal budgets; and thermals ultimately constrain packaging scaling. System architects simply cannot wait 10–15 years for each major semiconductor technology inflection to mature. Representing a roughly $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. A long‑term perspective is essential to align materials innovation with emerging device architectures — and to develop the tools and processes required to integrate both with manufacturable precision. At Applied Materials, together with our customers, we are charting a course across the next 3–4 generations, extending as far as 10 years down the roadmap. The angstrom era demands that we break down silos and bring together the industry’s best minds — from leading companies to leading academic institutions. If the problem is coupled, the solution must be coupled. If the timeline is compressed, the learning loop must be compressed. It’s not enough to just innovate — we must innovate how we innovate. EPIC: A Center and Platform for High‑Velocity Co‑Innovation This is the challenge that Applied Materials EPIC Center is designed to solve. Representing a roughly US $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. When it opens in 2026, it will deliver state‑of‑the‑art cleanroom capabilities built from the ground up to shorten the path from early‑stage research to full‑scale manufacturing. But the facilities are only one component of the model. EPIC is also a platform, an operating system for high-velocity co‑innovation that revolutionizes how ideas move from the lab to the fab. EPIC is a platform, an operating system for high-velocity co‑innovation that revolutionizes how ideas move from the lab to the fab.Applied Materials The EPIC model compresses the traditional workflow. Customer engineers work side‑by‑side with Applied technologists from day one — moving beyond isolated process optimization and downstream handoffs. Within a shared, secure environment, EPIC tightly integrates atomistic modeling, test vehicles, process development, validation, and metrology feedback. Constraints that once surfaced late in development are identified and addressed early. The result is a potentially 2x faster path that benefits the entire ecosystem under one roof: Chipmakers gain earlier access to Applied’s R&D portfolio, faster learning cycles, and accelerated transfer of next‑generation technologies into high‑volume manufacturing. Ecosystem partners gain earlier access to advanced manufacturing technology and collaboration opportunities that expand what is possible through materials innovation. Academic institutions gain opportunities to strengthen the lab‑to‑fab pipeline and help develop future semiconductor talent. Building on decades of co‑development, we are reinventing the innovation pipeline with our partners across logic, memory, and advanced packaging to deliver the next leap in energy‑efficient AI. Accelerating Advanced Logic Logic remains the engine of AI compute. In the angstrom era, however, system‑level gains are increasingly constrained by power and energy. Extending AI performance now depends on architectures that deliver more performance per watt — accelerating the move to 3D devices such as gate‑all‑around (GAA) transistors, which boost density within a compact footprint while preserving power efficiency. Architectures that deliver more performance per watt are accelerating the move to 3D devices such as gate‑all‑around (GAA) transistors, and further out, complementary FETs (CFETs), which push density scaling even more.Applied Materials These architectural shifts are unfolding at unprecedented scale, with the logic roadmap already extending beyond first‑generation GAA toward more advanced designs. One key example is GAA with backside power delivery, which relocates thick power lines to the backside of the wafer, reducing resistive losses and freeing front‑side routing for tighter logic cell integration. Another example brings adjacent GAA PMOS and NMOS transistors closer together while inserting a dielectric isolation wall between them to minimize electrical interference. Further out, complementary FETs (CFETs) push density scaling even more by stacking PMOS and NMOS devices directly atop one another. While these architectures deliver compelling gains in performance per watt and logic density without relying solely on tighter lithography, they significantly raise integration complexity. Manufacturing a single GAA device today can involve more than 2,000 tightly interdependent process steps. At the same time, wiring stacks continue to grow taller and denser to connect these advanced logic devices. Modern leading‑edge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring. Modern leading‑edge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring.Applied Materials At this level of complexity, the process steps used to create these precise 3D devices and wiring stacks cannot be optimized independently. Design and process must evolve in lockstep, and materials innovation and fabrication methods must advance alongside device architecture. EPIC’s co‑innovation model is designed to accelerate exactly this convergence — enabling logic compute to continue advancing the frontiers of AI at the pace the roadmap demands. Powering the Memory Roadmap At the same time, the AI computing era is fundamentally reshaping how data is generated, moved, and processed — making memory technologies, especially DRAM, central to delivering the energy‑efficient performance AI systems require. As models grow larger and more data‑hungry, the DRAM roadmap is shifting toward architectures that deliver higher density, greater bandwidth, and faster access per watt. At the DRAM cell level, AI performance requirements are driving a transition from 6F² buried‑channel array transistors (BCAT) to more compact 4F², and beyond that, architectures that move past what 2D scaling alone can deliver. Applied Materials At the DRAM cell level, this shift is driving a transition from 6F² buried‑channel array transistors (BCAT) to more compact 4F² architectures, which orient the transistor vertically to boost density and reduce chip area. Looking beyond 4F², sustaining gains in performance per watt will require moving past what 2D scaling alone can deliver. The industry is therefore turning to 3D DRAM, stacking memory cells vertically to add capacity within a constrained footprint. As these structures grow taller and aspect ratios intensify, high-mobility materials engineering in three dimensions becomes increasingly critical to performance and reliability. Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring. One emerging approach places select periphery functions beneath the DRAM array by bonding two wafers — one optimized for the DRAM cells and the other for CMOS logic — using multiple wiring layers. Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring.Applied Materials In parallel, DRAM performance is being extended by leveraging logic‑proven enhancers in the memory periphery. These include mobility boosters such as embedded silicon germanium and stress films, along with wiring upgrades like improved low‑k dielectrics and advanced copper interconnects. Memory manufacturers are also transitioning periphery transistors from planar devices to FinFET architectures, following the logic roadmap to further improve I/O speed. These valuable inflections are central to EPIC’s mission — where they can be co-developed and rapidly validated for next‑generation memory systems. Driving System Scaling With Advanced Packaging As data movement becomes the dominant energy cost in AI systems, advanced packaging has emerged as a critical lever for improving system‑level efficiency—shortening interconnect distances, increasing bandwidth density, and reducing the power required to move data between logic and memory. The rise of 3D packages such as high‑bandwidth memory (HBM) underscores why advanced packaging is becoming central to the AI era.Applied Materials High‑bandwidth memory (HBM) marks a major inflection along this path. By stacking DRAM dies — scaling to 16 layers and beyond — and placing memory much closer to the processor, HBM enables rapid access to ever‑larger working datasets. This delivers step‑function gains in both bandwidth and energy efficiency. More broadly, the rise of 3D packages such as HBM underscores why advanced packaging is becoming central to the AI era. Packaging now addresses system‑level constraints that logic and memory device scaling alone can no longer overcome. It also enables a move away from monolithic systems‑on‑chip toward chiplet‑based architectures, as AI workloads increasingly demand flexible designs that combine logic, memory, and specialized accelerators optimized for specific tasks. A vital technology powering this roadmap is hybrid bonding. With interconnect pitches approaching those of on‑chip wiring, conventional bumps and microbumps run into fundamental limits in density, power, and signal integrity. Hybrid bonding removes these barriers by allowing dramatically higher interconnect and I/O density, supporting a broad range of chiplet architectures — from memory stacking to tighter compute‑memory integration. EPIC tackles high‑value advanced‑packaging challenges through early, parallel co‑innovation across materials, integration, and manufacturing.Applied Materials As bonded structures like HBM stacks grow larger and more complex, warpage control, die placement, stack alignment, and thermal management become first‑order challenges. EPIC tackles these and other high‑value advanced‑packaging challenges through early, parallel co‑innovation across materials, integration, and manufacturing. Bringing It All Together Across logic, memory, and advanced packaging, our industry faces an ambitious roadmap that promises significant gains in energy efficiency for AI systems. But realizing that potential demands breakthrough materials innovation at a time when feature sizes are shrinking, interfaces are multiplying, and process interdependencies are escalating. These challenges cannot be solved on 10–15‑year timelines under the traditional relay‑race model. We must break down silos, align earlier across the ecosystem, and parallelize learning to keep pace with AI’s demands. In the AI era, progress will be defined by the speed at which lightbulb moments turn into manufacturing and commercialization reality. The only viable path forward is a new innovation model — and EPIC is how we are driving it.
Laboratory or in-field measurements are often considered the gold standard for certain aspects of power system design; however, measurement approaches always have limitations. Simulation can help overcome some of these limitations, including speeding up the design process, reducing design costs, and assessing situations that are often not feasible to measure directly. In this presentation, we will discuss two examples from the power system industry. The first case we will discuss involves corona performance testing of high-voltage transmission line hardware. Corona-free insulator hardware performance is critical for operation of transmission lines, particularly at 500 kV, 765 kV, or higher voltages. Laboratory mockups are commonly used to prove corona performance, but physical space constraints usually restrict testing to a partial single-phase setup. This requires establishing equivalence between the laboratory setup and real-world three-phase conditions. In practice, this can be difficult to do, but modern simulation capabilities can help. The second case involves submarine HVDC cables, which are commonly used for offshore wind interconnects. HVDC cables are often considered to be environmentally inert from an external electric field perspective (i.e., electric fields are contained in the cable, and the cable’s static magnetic fields induce no voltages externally). However, simulation demonstrates that ocean currents moving through the static magnetic field satisfy the relative motion requirement of Faraday’s law. Thus, externally induced electric fields can exist around the cable and are within a range detectable by various aquatic species. Key Takeaway: Learn how to use modern simulation to translate single-phase laboratory corona mockups into accurate three-phase real-world performance for 500 kV and 765 kV systems. Explore the physics behind how ocean currents interacting with HVDC submarine cables create induced electric fields—a phenomenon often overlooked but detectable by aquatic species. Gain actionable insights into how to leverage simulation to reduce design costs and bypass the physical space constraints that often stall traditional testing. See a practical application of electromagnetic theory as we demonstrate how relative motion in static magnetic fields necessitates simulation where direct measurement is unfeasible. Register now for this free webinar!