Abstract:We improve design quality factors of slotted photonic crystal nanocavities embedded in electro-optic polymers (EOPs), which enables control of resonant wavelengths without the use of light-absorbing free carriers. We form nanocavities by modifying single- and double-slotted line-defect waveguides with lattice-constant modulations analytically determined based on dispersions of the waveguides. A double-slotted nanocavity achieves a fourfold increase in Q factor (36 million) compared to a single-slotted nanocavi… Show more
“…Owing to this design approach, the loss determined by the cavity design itself is significantly reduced and the median of the experimental Q factors was improved by about 1 million (from 6.9 × 10 6 to 7.8 × 10 6 ). We believe that optimization with aid of a deep neural network can also increase the experimental Q factors of other types of nanocavities, [29][30][31][32] which will be useful in various scientific and engineering fields.…”
Photonic crystal (PC) nanocavities with ultra-high quality (Q) factors and small modal volumes enable advanced photon manipulations, such as photon trapping. In order to improve the Q factors of such nanocavities, we have recently proposed a cavity design method based on machine learning. Here, we experimentally compare nanocavities designed by using a deep neural network with those designed by the manual approach that enabled a record value. Thirty air-bridge-type two-dimensional PC nanocavities are fabricated on silicon-on-insulator substrates, and their photon lifetimes are measured. The realized median Q factor increases by about one million by adopting the machine-learning-based design approach.
“…Owing to this design approach, the loss determined by the cavity design itself is significantly reduced and the median of the experimental Q factors was improved by about 1 million (from 6.9 × 10 6 to 7.8 × 10 6 ). We believe that optimization with aid of a deep neural network can also increase the experimental Q factors of other types of nanocavities, [29][30][31][32] which will be useful in various scientific and engineering fields.…”
Photonic crystal (PC) nanocavities with ultra-high quality (Q) factors and small modal volumes enable advanced photon manipulations, such as photon trapping. In order to improve the Q factors of such nanocavities, we have recently proposed a cavity design method based on machine learning. Here, we experimentally compare nanocavities designed by using a deep neural network with those designed by the manual approach that enabled a record value. Thirty air-bridge-type two-dimensional PC nanocavities are fabricated on silicon-on-insulator substrates, and their photon lifetimes are measured. The realized median Q factor increases by about one million by adopting the machine-learning-based design approach.
“…The photonic crystal slot waveguide was formed by removing a row of air holes from the crystal and etching an air slot with width of 100 nm and depth of 400 nm. The photonic crystal slot waveguide provides a broadband waveguiding mode with an electric field that is mainly confined within the air slot region because of the continuity requirement of the electric displacement vector at the interface . The photonic crystal nanocavity structure was fabricated by removing three adjacent air holes and adjusting the radii and relative locations of the surrounding air holes.…”
Electronic–photonic hybrid integrated circuits represent the essential basis of next‐generation ultrahigh‐speed information processing chips, which will require ultrafast and ultralow‐energy‐consumption electronic and photonic information interconversion and modulation, i.e., on‐chip dual electro‐optic and optoelectric modulation. However, this type of modulation has not been realized to date because of an absence of materials that demonstrate sufficient intrinsic electro‐optic and optoelectric responses simultaneously. On‐chip dual electro‐optic and optoelectric modulation is experimentally realized here based on refractive index variation of a ZnO nanowire driven by the gate voltage and electrical conductivity changes in this nanowire caused by the strong localized light field of a silicon nitride photonic crystal nanocavity mode. A low gate voltage of 1.5 V induces a large shift of 7 nm in the Fano‐like resonance wavelength and modulates the propagation state of an 800 nm light signal with a large modulation depth of 75%. Additionally, weak signal light with power as low as 0.4 µW induces a modulation depth of 60% in the electric signal. On‐chip conversion between electronic and photonic signals thus is achieved. This work paves the way toward realization of novel nanoscale multifunctional optoelectronic integrated devices and provides an on‐chip platform for the study of new optoelectronic functional materials.
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