Nano-opto-electro-mechanical switches operated at CMOS-level voltages

被引:71
作者
Haffner, Christian [1 ,2 ,3 ]
Joerg, Andreas [1 ]
Doderer, Michael [1 ]
Mayor, Felix [1 ,6 ,7 ]
Chelladurai, Daniel [1 ]
Fedoryshyn, Yuriy [1 ]
Roman, Cosmin Ioan [4 ]
Mazur, Mikael [5 ]
Burla, Maurizio [1 ]
Lezec, Henri J. [3 ]
Aksyuk, Vladimir A. [3 ]
Leuthold, Juerg [1 ]
机构
[1] Swiss Fed Inst Technol, IEF, CH-8092 Zurich, Switzerland
[2] Univ Maryland, Inst Res Elect & Appl Phys, Maryland NanoCtr, College Pk, MD 20742 USA
[3] NIST, Phys Measurement Lab, Gaithersburg, MD 20899 USA
[4] Swiss Fed Inst Technol, Micro & Nanosyst, CH-8092 Zurich, Switzerland
[5] Chalmers Univ Technol, Dept Microtechnol & Nanosci, Photon Lab, Gothenburg, Sweden
[6] Stanford Univ, Dept Appl Phys, 348 Via Pueblo Mall, Stanford, CA 94305 USA
[7] Stanford Univ, Ginzton Lab, 348 Via Pueblo Mall, Stanford, CA 94305 USA
关键词
HIGH-SPEED; WAVE-GUIDE; SILICON; MODULATOR; TRANSDUCTION; PLASMONICS; COMPACT;
D O I
10.1126/science.aay8645
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Combining reprogrammable optical networks with complementary metal-oxide semiconductor (CMOS) electronics is expected to provide a platform for technological developments in on-chip integrated optoelectronics. We demonstrate how opto-electro-mechanical effects in micrometer-scale hybrid photonic-plasmonic structures enable light switching under CMOS voltages and low optical losses (0.1 decibel). Rapid (for example, tens of nanoseconds) switching is achieved by an electrostatic, nanometer-scale perturbation of a thin, and thus low-mass, gold membrane that forms an air-gap hybrid photonic-plasmonic waveguide. Confinement of the plasmonic portion of the light to the variable-height air gap yields a strong opto-electro-mechanical effect, while photonic confinement of the rest of the light minimizes optical losses. The demonstrated hybrid architecture provides a route to develop applications for CMOS-integrated, reprogrammable optical systems such as optical neural networks for deep learning.
引用
收藏
页码:860 / +
页数:35
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