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Co-design of 3D Vertical Back-end-of-line Ferroelectric Memcapacitors and In-Memory Computing Circuits for Extreme Energy Efficiency in Computing
Funded by the National Science Foundation (FuSe2)
Energy use for computing is ever-increasing, particularly with the growth of artificial intelligence (AI) and machine learning. Modern AI requires large datacenters that house thousands of specialized computers that are used to train AI models and generate responses to queries. These datacenters require huge amounts of electricity, and they generate so much heat that extensive cooling systems are needed to prevent the computers from overheating. Networks of datacenters already exist to support the cloud-based computing that the world has come to rely on for everything from communication to banking to transportation infrastructure. However, the growth of AI will dramatically increase the need for such datacenters. According to a 2024 report from the International Energy Agency, an AI-enabled ChatGPT query uses almost ten times more energy than a standard Google search. Furthermore, the report predicts exponential growth in the AI industry, increasing AI's electricity demand by at least 10x from 2023 to 2026. This research project will investigate ways to fundamentally redesign the computing systems that support AI, from the semiconductor materials and devices to the computing circuits and architectures. The project aims to improve the energy efficiency of these systems by orders of magnitude. Such dramatic reductions in the energy demand for AI would positively impact society by reducing the strain on the electricity grid and mitigating the impacts of AI on climate change. Furthermore, this project supports an Education and Workforce Development plan that is a collaboration with community colleges and the Micro Nano Technology Education Center, an NSF Advanced Technology Education Center dedicated to increasing the semiconductor workforce.
In this project, a team of investigators will pursue interdisciplinary research encompassing materials, devices, circuits, and architecture co-designs to improve the energy efficiency of AI and machine learning computing hardware. Most of the computing resources for machine learning algorithms are used by multiply-and-accumulate (MAC). The regularity and parallelism of MACs make them very suitable for hardware acceleration. However, conventional random-access memory requires row-by-row accesses, and fetching billions of weights in this manner consumes substantial energy. One solution to this bottleneck is eliminating the row-by-row memory access by designing hardware systems where the computing occurs inside the memory, referred to as "in-memory computing". In-memory computing is achieved by inserting a small computing circuit in each memory cell. A promising new memory element, the "memcapacitor", has been proposed for this purpose, but few devices have been experimentally demonstrated. The technical aims of this project can be broken into three major areas: 1) Create a vertical memcapacitor device that can be fabricated in the backend of the CMOS process for monolithic 3D integration; 2) Create memcapacitor-based in-memory computing circuits; and 3) Create a deep neural network accelerator with fully analog-datapath and digital-control.
Transforming the Future of Flexible Transistors with Photonic Processing
Funded by the National Science Foundation (CAREER)
Electronic devices have become an integral part of human life. From cell phones and medical devices to cars and traffic monitors, we rely on electronic circuits to keep us connected, healthy, and safe. While the vast majority of electronics today are built using hard, rigid materials, electronics that are soft and flexible are desirable for many applications. For example, wearable and implantable biomedical sensors will benefit from advances in flexible electronics that can bend and stretch to conform to the body. This research aims to make transformative changes in the performance, stability, and durability of flexible electronics. The proposed activities will generate a novel low-cost approach to fabricating flexible circuits. Results from this work will enable this novel fabrication approach to be used in a wide array of next-generation flexible circuit applications including displays, biomedical sensors, and durable lightweight electronics for military applications. In addition, a synergistic combination of research and integrated education activities are proposed that aim to inspire the next generation of electrical engineers and increase engagement of women and under-represented minorities in engineering.
Currently, flexible circuit technology is readily available for passives (metals) but in applications such as displays and sensors, flexible transistors (semiconductors) are needed. The thermal properties and solvent compatibility of plastic substrates place severe limitations on the fabrication of flexible thin-film transistors (TFTs), so TFT performance is sacrificed to achieve flexible, lightweight circuits. To overcome the fundamental tradeoff between the processing requirements of substrates and TFTs, we will leverage new advancements in photonic processing technology to produce high-performance flexible TFTs. Photonic curing uses short, intense pulses of broadband light to heat a thin film, while most of the substrate remains near room temperature. However, there is a compelling need to better understand the physics of the photonic curing process and the quality of photonically cured semiconductor TFTs. This research will pursue the following four objectives: (1) Predict the 3D thermal profile in circuits during photonic curing, and understand how geometry, device layout, proximity effects, and material properties impact curing temperature; (2) Determine the impact of photonic curing on trap states and electron transport in oxide semiconductors on plastic substrates by combining electrical and materials characterizations with a model of the density of electronic traps in the semiconductor; (3) Identify failure mechanisms of the plastic substrate to enable the oxide semiconductor to be more aggressively cured without damaging the substrates, and (4) Analyze multiple simultaneous stress factors in flexible oxide TFTs to improve their durability using a novel testing scheme that mimics a realistic operating environment. This research will advance science by shedding new light on the sub-millisecond interactions between light, heat, and thin-film semiconductor materials. New models that predict the thermal profiles during large-area photonic curing will enable this technology to be used in a wide array of flexible circuit applications. Furthermore, these activities will also build a firm foundation for the PI to pursue her long-term career goal of leading high-impact collaborative projects with physicians and health care providers. She aims to create innovative tools leading to a paradigm shift in how health care is delivered in fields such as emergency triage, battlefield wound monitoring, implantable neural interfaces, and soft wireless pediatric devices.