光学イノベーション：UDエンジニアリングの研究者は、最先端のコンピューティングデバイスを作成している。(Optical Innovations：UD Engineering’s Tingyi Gu, fellow researchers are creating state-of-the-art computing devices)

UDエンジニアリングの研究者は、最先端のコンピューティングデバイスを作成している。 UD Engineering’s Tingyi Gu, fellow researchers are creating state-of-the-art computing devices

2022-12-12 デラウェア大学 (UD)

＜関連情報＞

• https://www.udel.edu/udaily/2022/december/tingyi-gu-electrical-computer-engineering-photonics-optics/
• https://www.nature.com/articles/s41467-022-29856-7
• https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202108261

通信波長域での画像分類のための集積型フォトニックメタシステム Integrated photonic metasystem for image classifications at telecommunication wavelength

Zi Wang,Lorry Chang,Feifan Wang,Tiantian Li & Tingyi Gu
Nature Communications  Published:19 April 2022
DOI:https://doi.org/10.1038/s41467-022-29856-7

Abstract

Miniaturized image classifiers are potential for revolutionizing their applications in optical communication, autonomous vehicles, and healthcare. With subwavelength structure enabled directional diffraction and dispersion engineering, the light propagation through multi-layer metasurfaces achieves wavelength-selective image recognitions on a silicon photonic platform at telecommunication wavelength. The metasystems implement high-throughput vector-by-matrix multiplications, enabled by near 10³ nanoscale phase shifters as weight elements within 0.135 mm² footprints. The diffraction manifested computing capability incorporates the fabrication and measurement related phase fluctuations, and thus the pre-trained metasystem can handle uncertainties in inputs without post-tuning. Here we demonstrate three functional metasystems: a 15-pixel spatial pattern classifier that reaches near 90% accuracy with femtosecond inputs, a multi-channel wavelength demultiplexer, and a hyperspectral image classifier. The diffractive metasystem provides an alternative machine learning architecture for photonic integrated circuits, with densely integrated phase shifters, spatially multiplexed throughput, and data processing capabilities.

集積型フォトニックメモリを目指した層状インジウムセレナイド間の構造相転移 Structural Phase Transitions between Layered Indium Selenide for Integrated Photonic Memory

Tiantian Li,Yong Wang,Wei Li,Dun Mao,Chris J. Benmore,Igor Evangelista,Huadan Xing,Qiu Li,Feifan Wang,Ganesh Sivaraman,Anderson Janotti,Stephanie Law,Tingyi Gu
Advanced Materials  Published: 18 April 2022
DOI:https://doi.org/10.1002/adma.202108261

Abstract

The primary mechanism of optical memoristive devices relies on phase transitions between amorphous and crystalline states. The slow or energy-hungry amorphous–crystalline transitions in optical phase-change materials are detrimental to the scalability and performance of devices. Leveraging an integrated photonic platform, nonvolatile and reversible switching between two layered structures of indium selenide (In₂Se₃) triggered by a single nanosecond pulse is demonstrated. The high-resolution pair distribution function reveals the detailed atomistic transition pathways between the layered structures. With interlayer “shear glide” and isosymmetric phase transition, switching between the α- and β-structural states contains low re-configurational entropy, allowing reversible switching between layered structures. Broadband refractive index contrast, optical transparency, and volumetric effect in the crystalline–crystalline phase transition are experimentally characterized in molecular-beam-epitaxy-grown thin films and compared to ab initio calculations. The nonlinear resonator transmission spectra measure of incremental linear loss rate of 3.3 GHz, introduced by a 1.5 µm-long In₂Se₃-covered layer, resulted from the combinations of material absorption and scattering.