Visually Adjusting Coupling Conditions in Light-Emitting Micro-Components

Liang, Dingxin; Zhang, Xining; Li, Mengjie; Lin, Zhihong; Dai, Hao; Wu, Zhijun; Pu, Jixiong

doi:10.1109/lpt.2019.2929838

Cited by 5 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For certain applications, such as plasmonic waveguide and plasmon routers, − it may be preferable to have more than one nanowire per site or even completely lumped together with no gaps . The capillary force acting between the nanowires can be utilized to achieve arrays of multiple nanowires.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, through the knocking down method, there is a denser array of nanowires with 100 nm interwire distance, but the process requires very precise control of rolling pressure and speed. In addition, for plasmonic waveguide and plasmon routers, − it may be preferable to have an array of more than one nanowire per site or even completely lumped together with no gaps, which is hard to achieve through the knocking down method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Elastocapillary Force Induced Alignment of Large Area Planar Nanowires

Jung

Choi

Huang

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Achieving large scale precise positioning of the vapor−liquid−solid (VLS) nanowires is one of the biggest challenges for mass production of nanowire-based devices. Although there have been many noteworthy progresses in postgrowth nanowire alignment method development over the past few decades, these methods are mostly suitable for low density applications only. For high density applications such as transistors, both high yield and density are required. Here, we report an elastocapillary force-induced nanowire-aligning method that is extremely simple, clean, and can achieve single/multiple nanowire arrays with up to 98.8% yield and submicron pitch between the nanowires.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elastocapillary Force Induced Alignment of Large Area Planar Nanowires

Jung

Choi

Huang

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

“…This is a technology problem that can be resolved in practice, so it is not a crucial obstacle. Several logic operations, on the other hand, have been experimentally implemented based on various optical waveguides and components [31][32][33][34][35][36][37][38].…”

Section: Xnormentioning

confidence: 99%

K-Shaped Silicon Waveguides for Logic Operations at 1.55 μm

Kotb

Zoiros

2022

Electronics

View full text Add to dashboard Cite

Silicon has properties that make it the preferable semiconductor material for realizing a wide suite of electronic devices. In this paper, all basic optical logic operations, including XOR, AND, OR, NOT, NOR, XNOR, and NAND, are demonstrated by means of simulation using K-shaped compact silicon waveguides operated at the 1.55 μm telecommunications wavelength. This waveguide comprises three waveguide strips, all made of silicon printed on silica. By adjusting the phase of the incident beams, the pursued logic operations can be realized. To evaluate how well the considered operations are performed, the contrast ratio (CR) is employed as a figure of merit. Compared to other reported waveguides, the suggested K-shaped waveguide achieves higher CRs and a speed of the order of 120 Gb/s.

show abstract

Energy Attenuation Prediction of Dye-Doped PMMA Microfibers by Backpropagation Neural Network

Liu

Han

et al. 2022

IEEE Photonics J.

View full text Add to dashboard Cite

To figure out the energy attenuation of micro/nanofibers (MNFs) more flexibly and conveniently, a backpropagation neural network (BPNN) is proposed to forecast the output intensity of rhodamine B (RhB) doped polymer microfibers (PMFs). According to the diameter, doping concentration, and propagation distance (L), we realize the L-dependence of output energy predictions for the excitation light (I E ) and fluorescence (I F ) of the doped PMFs. Hundreds of propagation distance-intensity data pairs acquired from dozens of RhB doped PMFs are used for the BPNN training. The prediction ability of the model is evaluated by the root-mean-square error (RMSE), the mean absolute percentage error (MAPE), and R 2 . The output intensity prediction performance of BPNN is compared with the traditional exponential-fitting (EF) method. The prediction results indicate that the two-hidden-layer network with one and seventeen neurons respectively provides the best performance. After training, BPNN gives a good intensity prediction for both the I E (RMSE=3.16 × 10 -2 , MAPE=7.3%, and R 2 =0.9802) and the I F (RMSE=0.91 × 10 -2 , MAPE=0.89%, and R 2 =0.9696) from the output end of the PMF with different diameters and doping concentrations. The energy losses of the two kinds of light from different doped PMFs are also calculated based on the predicted values, which are similar to the ones obtained from the EF method. The approach based on the BPNN prediction for the energy attenuation of the PMFs shows superiority in flexibility and applicability toward the traditional methods, which could promote the optimal design of the MNF devices and the practical application.

show abstract

Visually Adjusting Coupling Conditions in Light-Emitting Micro-Components

Cited by 5 publications

References 22 publications

Elastocapillary Force Induced Alignment of Large Area Planar Nanowires

Elastocapillary Force Induced Alignment of Large Area Planar Nanowires

K-Shaped Silicon Waveguides for Logic Operations at 1.55 μm

Energy Attenuation Prediction of Dye-Doped PMMA Microfibers by Backpropagation Neural Network

Contact Info

Product

Resources

About