Zobrazeno 1 - 10
of 300
pro vyhledávání: '"Gao, Weilu"'
Autor:
Fan, Jichao, Hillam, Benjamin, Guo, Cheng, Fujinami, Hiroyuki, Koki, Shiba, Xie, Haoyu, Chen, Ruiyang, Yanagi, Kazuhiro, Gao, Weilu
The interaction of circularly polarized light with chiral matter and functional devices enables novel phenomena and applications. Recently, wafer-scale solid-state single-enantiomer carbon nanotube (CNT) films have become feasible and are emerging as
Externí odkaz:
http://arxiv.org/abs/2410.08586
Autor:
Tang, Yingheng, Chen, Ruiyang, Lou, Minhan, Fan, Jichao, Yu, Cunxi, Nonaka, Andy, Yao, Zhi, Gao, Weilu
Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. A
Externí odkaz:
http://arxiv.org/abs/2409.06234
All-optical and fully reconfigurable diffractive optical neural network (DONN) architectures are promising for high-throughput and energy-efficient machine learning (ML) hardware accelerators for broad applications. However, current device and system
Externí odkaz:
http://arxiv.org/abs/2408.01404
Autor:
Xu, Rui, Doumani, Jacques, Labuntsov, Viktor, Hong, Nina, Samaha, Anna-Christina, Tu, Weiran, Tay, Fuyang, Blackert, Elizabeth, Luo, Jiaming, Tahchi, Mario El, Gao, Weilu, Lou, Jun, Yomogida, Yohei, Yanagi, Kazuhiro, Saito, Riichiro, Perebeinos, Vasili, Baydin, Andrey, Kono, Junichiro, Zhu, Hanyu
Chiral carbon nanotubes (CNTs) are direct-gap semiconductors with optical properties governed by one-dimensional excitons with enormous oscillator strengths. Each species of chiral CNTs has an enantiomeric pair of left- and right-handed CNTs with nea
Externí odkaz:
http://arxiv.org/abs/2407.04514
The ability to design and dynamically control chiroptical responses in solid-state matter at wafer scale enables new opportunities in various areas. Here we present a full stack of computer-aided designs and experimental implementations of a dynamica
Externí odkaz:
http://arxiv.org/abs/2406.13190
To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framework, which consists of (1) precise and differentiable optical physics ke
Externí odkaz:
http://arxiv.org/abs/2306.11268
Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in com
Externí odkaz:
http://arxiv.org/abs/2304.12985
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low
Externí odkaz:
http://arxiv.org/abs/2304.01500
Autor:
Wais, Michael, Bagsican, Filchito Renee G., Komatsu, Natsumi, Gao, Weilu, Serita, Kazunori, Murakami, Hironaru, Held, Karsten, Kawayama, Iwao, Kono, Junichiro, Battiato, Marco, Tonouchi, Masayoshi
The one-dimensional confinement of quasiparticles in individual carbon nanotubes (CNTs) leads to extremely anisotropic electronic and optical properties. In a macroscopic ensemble of randomly oriented CNTs, this anisotropy disappears together with ot
Externí odkaz:
http://arxiv.org/abs/2303.01866
Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs
Externí odkaz:
http://arxiv.org/abs/2302.10905