The University of Washington was awarded a $315,998 Project Grant from the National Science Foundation to advance technology enabling advanced high-resolution full-color displays through new color conversion technologies. Under the NSF's Technology, Innovation, and Partnerships program, the University will develop a micro-color converter technology that combines micro-patterning methods and perovskite semiconductors to significantly reduce the number of steps required in micro-LED display...
This National Science Foundation (NSF) Engineering program (CFDA 47.041) Project Grant award of $425,000 to the University of Washington is focused on developing high-speed perovskite light-emitting diodes (LEDs) for optical interconnects and data transmission. The key objectives are to: Engineer perovskite semiconductor materials and device designs to minimize resistance and increase LED modulation bandwidth. Optimize modulation formats using data-driven learning for high-bandwidth operation....
This $3,000,000 Project Grant awarded by the National Science Foundation (NSF) Engineering Program (CFDA 47.041) supports research to develop a novel lamination approach for halide perovskite semiconductors that enables new device architectures and material combinations for applications like solar cells, LEDs, and other optoelectronic devices. The research aims to understand, model, and control the process-structure-property relationships during halide perovskite semiconductor manufacturing...
This $400,000 National Science Foundation project grant supports research at Florida State University to develop electrically driven single emissive layer white light emitting diodes (WLEDs) based on solution processable halide perovskites and perovskite-related hybrid materials from September 1, 2022 to August 31, 2025. The grant is funded through NSF's Engineering Directorate (ENG) and the CFDA program for Engineering aims to improve engineering research and education. Specifically, the...
This $271,320 National Science Foundation project grant supports research at Arizona State University to develop machine learning-assisted ultrafast physical vapor deposition of high-quality, large-area functional thin films. The goal is to apply machine learning algorithms to optimize thin film growth conditions and accelerate the development of thin films with targeted electronic and optical properties. This will enable faster and lower-cost manufacturing of functional oxides, chalcogenides...
This $499,394 award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research by the University of Houston System to develop machine learning models to guide the discovery of novel phosphor materials for energy-efficient phosphor-converted LED (PC-LED) lighting. The key objectives are to: 1) develop machine learning models to predict the optical properties and chemical stability of bulk ceramic phosphor powders, and 2) synthesize...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $400,000 Project Grant to The Regents of the University of Colorado to support research titled "SUPERLATTICE ARCHITECTURES FOR EFFICIENT AND STABLE PEROVSKITE LEDS" from September 1, 2021 through August 31, 2025. The grant funds research under the NSF Engineering program (CFDA 47.041) to develop superlattice architectures for perovskite light-emitting diodes with improved efficiency and...
This National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) supports the development of a multiscale simulation tool that uses machine learning to predict the deformation behavior of semiconductors under light exposure. The $316,455 award to North Carolina State University will establish an electronic-to-mesoscale modeling framework to advance the understanding of "photoplasticity" - the phenomenon where light can cause materials to harden...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $750,000 to Northeastern University to develop innovative engineered photonic materials using a data-driven deep learning approach. The research aims to accelerate the discovery, design, and implementation of new photonic metamaterials with tailored optical properties for applications in areas such as lasers, optical communications, quantum computing, and...
This $200,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to advance the field of semiconductor manufacturing by addressing a critical challenge in extreme ultraviolet (EUV) photolithography. The research, conducted by the University of Washington over a two-year period from June 2025 to May 2027, will focus on developing new photoresist materials and uncovering the mechanisms behind the crosslinking and chemical etching of ultrathin...
This $599,999 National Science Foundation project grant supports research at the University of Washington to accelerate manufacturing and realization of perovskite micro-light emitting diode displays through data-driven learning approaches. The goal is to advance augmented- and virtual-reality display technologies by exploring new perovskite materials and optimizing device performance with machine learning. Specifically, the research develops a neural network-based machine learning process to facilitate manufacturing of high-quality, stable perovskite micro-LED displays. It identifies material and device features from various databases to feed machine learning models, guiding solution processing of perovskites. Additionally, the project aims to address patterning challenges through developing micro-patterning photolithography technology. This work supports the NSF Engineering program's objective to foster innovation in engineering research areas with potential economic and societal impacts.