Vol. 180
Latest Volume
All Volumes
PIER 180 [2024] PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2024-09-23
PIER
Vol. 180, 55-60, 2024
download: 179
Topology-Optimized Plasmonic Nanoantenna for Efficient Single-Photon Extraction
Min Chen , Lian Shen , Yifei Hua , Zijian Qin and Huaping Wang
Quantum emitters coupled to plasmonic nanostructures can act as extremely bright single-photon sources. Interestingly, the mode volumes supported by the plasmonic nanostructures can be several orders of magnitude smaller than the cubic wavelength, which leads to dramatically enhanced light-matter interactions and drastically increased photon emission. However, the requirements of a small mode volume for emission speed-up are always contradictory with a sufficiently large mode volume for efficient extraction, especially in a single architecture. Here, we report the design of a topology-optimized plasmonic nanoantenna to alleviate the above limitation which could greatly enhance far-field photon extraction. The plasmonic nanoantenna is composed of an optimized gold pattern and a silicon nitride substrate, with a nanohole in the center of the gold pattern. Our design is based on density-based topology optimization and is inherently robust to dimensions and fabrication errors. As a result, the normalized extraction decay rate (γe⁄γ0) can reach 5.48 at a wavelength of 517 nm if an objective lens with a numerical aperture of 0.45 is utilized. Plasmonic nanostructures can be obtained with a small mode volume of about 5 × 10-21 m3, while emission speed-up could still be achieved. The proposed method to alleviate the contradiction of plasmonic mode volume could brighten the prospects for future integration of single-photon sources into photonic quantum networks and applications in quantum information science.
Topology-optimized Plasmonic Nanoantenna for Efficient Single-photon Extraction
2024-09-18
PIER
Vol. 180, 25-53, 2024
download: 309
Alternative Plasmonic Materials for Biochemical Sensing: a Review (Invited Review)
Leonid Yu. Beliaev , Andrei V. Lavrinenko and Osamu Takayama
Optical materials whose permittivity becomes negative for certain wavelength ranges, so-called plasmonic materials, have been widely used for biochemical sensing applications to detect a wide variety of analytes from chemical agents to protein biomarkers. Since many analytes are or contain nanoscale objects, they interact very weakly with light. Thus, light confinement is a key to improving sensitivity. Using metal or plasmonic nanostructures is a natural solution to confine light and boost light-matter interactions. As there are several different optical sensing schemes, such as refractometric sensing, fluorescence-labeled sensing, and vibrational spectroscopy, whose operating wavelength spans from ultraviolet to mid-infrared wavelength regions, some plasmonic materials are superior to others for certain wavelength regions. In this article, we review current progress on alternative plasmonic materials, other than gold, silver, and aluminum, used in biochemical sensing applications. We cover a wide variety of plasmonic material platforms, such as transparent conductive oxides, nitrides, doped semiconductors, polar materials, two-dimensional, van der Waals materials, transition metal dichalcogenides, and plasmonic materials for ultraviolet wavelengths.
Alternative Plasmonic Materials for Biochemical Sensing: A Review (Invited Review)
2024-09-04
PIER
Vol. 180, 13-24, 2024
download: 361
Measurement of Time-Range-Angle-Dependent Beam Patterns of Frequency Diverse Arrays (Invited)
Haochi Zhang , Lepeng Zhang , Shengheng Liu , Zihuan Mao , Yahui Ma , Pei Hang He , Wen Yi Cui , Yi Fei Huang , Qi Yang and Tie-Jun Cui
Frequency diverse arrays (FDAs) have drawn great attention because they can provide a time-range-angle-dependent beam pattern that has many promising potential applications in navigation and radar systems. However, due to the limitations of measurement systems, this attractive beam pattern has not been experimentally observed. Here, a far-field measurement system for the time-range-angle beam pattern of FDA is proposed by improving the existing near-field mapping system. Without loss of generality, two types of time-range-angle-dependent beam patterns for FDA systems with different frequency sets are observed using the proposed far-field measurement system. The high efficiency and accuracy of the proposed system is verified by good agreement between the measured and simulated results. This work marks significant progress toward the practical implementation and application of FDAs.
Measurement of Time-Range-Angle-Dependent Beam Patterns of Frequency Diverse Arrays (Invited)
2024-09-01
PIER
Vol. 180, 1-11, 2024
download: 337
Highly Accurate and Efficient 3D Implementations Empowered by Deep Neural Network for 2DLMs -Based Metamaterials
Naixing Feng , Huan Wang , Xuan Wang , Yuxian Zhang , Chao Qian , Zhixiang Huang and Hongsheng Chen
Streamlining the on-demand design of metamaterials, both forward and inverse, is highly demanded for unearthing complex light-matter interaction. Deep learning, as a popular data-driven method, has recently found to largely alleviate the time-consuming and experience-orientated features in widely-used numerical simulations. In this work, we propose a convolution-based deep neural network to implement the inverse design and spectral prediction of a broadband absorber, and deep neural network (DNN) not only achieves highly-accurate results based on small data samples, but also converts the one-dimensional (1D) spectral sequence into a 2D picture by employing the Markov transition field method so as to enhance the variability between spectra. From the perspective of a single spectral sample, spectral samples carry not enough information for neural network due to the constraints of the number of sampling points; from the perspective of multiple spectral samples, the gap between different spectral samples is very small, which can hinder the performance of the reverse design framework. Markov transition field method can enhance the performance of the model from those two aspects. The experimental results show that the final value of the soft required accuracy of the one-dimensional fully connected neural network model and the two-dimensional residual neural network model differ by nearly 1%, the final value of the soft accuracy of the one-dimensional residual neural network model is 97.6%. The final value of the two-dimensional residual neural network model model is 98.5%. The model utilises a data enhancement approach to improve model accuracy and also provides a key reference for designing two-dimensional layered materials (2DLMs) based metamaterials with on-demand properties before they are put into manufacturing.
Highly Accurate and Efficient 3D Implementations Empowered by Deep Neural Network for 2DLMs-based Metamaterials