A Multi-Objective Genetic Algorithm Approach for Silicon Photonics Design

被引:3
|
作者
Mahrous, Hany [1 ]
Fedawy, Mostafa [1 ,2 ]
Abboud, Mira [3 ]
Shaker, Ahmed [4 ]
Fikry, W. [4 ]
Gad, Michael [4 ]
机构
[1] Arab Acad Sci & Technol & Maritime Transport, Fac Engn, Elect & Commun Dept, Cairo 2033, Egypt
[2] Arab Acad Sci & Technol & Maritime Transport, Ctr Excellence Nanotechnol, Cairo 2033, Egypt
[3] Lebanese Univ, Fac Sci, Dept Comp Sci, Fanar 2611, Lebanon
[4] Ain Shams Univ, Fac Engn, Engn Phys & Math Dept, Cairo 11517, Egypt
关键词
integrated optics; silicon photonics; silicon on insulator; interleaver; deinterleaver; ring resonators; genetic algorithm; optimization; RING-RESONATOR; SENSITIVITY; LINES;
D O I
10.3390/photonics11010080
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
A multi-objective genetic algorithm approach is formulated to optimize the design of silicon-photonics complex circuits with contradicting performance metrics and no closed-form expression for the circuit performance. A case study is the interleaver/deinterleaver circuit which mixes/separates optical signals into/from different physical channels while preserving the wavelength-division-multiplexing specifications. These specifications are given as channel spacing of 50 GHz, channel 3-dB bandwidth of at least 20 GHz, channel free spectral range of 100 GHz, crosstalk of -23 dB or less, and signal dispersion less than 30 ps/nm. The essence of the proposed approach lies in the formulation of the fitness functions and the selection criteria to optimize the values of the three coupling coefficients, which govern the circuit performance, in order to accommodate the contradicting performance metrics of the circuit. The proposed approach achieves the optimal design in an incomparably short period of time when contrasted with the previous tedious design method based on employing Z-transform and visual inspection of the transmission poles and zeros.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] A multi-objective genetic algorithm approach to the design of cellular manufacturing systems
    Solimanpur, M
    Vrat, P
    Shankar, R
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2004, 42 (07) : 1419 - 1441
  • [2] A multi-objective genetic algorithm approach to rule mining for affective product design
    Fung, K. Y.
    Kwong, C. K.
    Siu, K. W. M.
    Yu, K. M.
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (08) : 7411 - 7419
  • [3] Solving multi-objective cell design problem: An evolutionary genetic algorithm approach
    Pattanaik, L.N.
    Jain, R.K.
    Mehta, N.K.
    International Journal of Manufacturing Technology and Management, 2007, 11 (02) : 251 - 273
  • [4] Multi-objective design of reliable systems by genetic algorithm
    Echtle, K.
    Eusgeld, I.
    Hirsch, D.
    SAFETY AND RELIABILITY FOR MANAGING RISK, VOLS 1-3, 2006, : 1625 - +
  • [5] A multi-objective grouping genetic algorithm for modular design
    Tseng, Hwai-En
    Chang, Chien-Cheng
    Lee, Shih-Chen
    Li, Tzu-Hui
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2023, 237 (03) : 377 - 391
  • [6] Multi-objective Genetic Algorithm for Interior Lighting Design
    Plebe, Alice
    Pavone, Mario
    MACHINE LEARNING, OPTIMIZATION, AND BIG DATA, MOD 2017, 2018, 10710 : 222 - 233
  • [7] Multi-objective production planning: A genetic algorithm approach
    Wu, Y
    Lai, KK
    PROCEEDINGS OF THE FIFTH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1 AND 2, 2000, : A929 - A932
  • [8] A multi-objective genetic algorithm for robust design optimization
    Li, Mian
    Azarm, Shapour
    Aute, Vikrant
    GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, 2005, : 771 - 778
  • [9] Multi-objective Approach to Grillage Optimization with Genetic Algorithm
    Maciunas, D.
    MECHANIKA 2012: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE, 2012, : 176 - 181
  • [10] A micro multi-objective genetic algorithm for multi-objective optimizations
    Liu, G. P.
    Han, X.
    CJK-OSM 4: THE FOURTH CHINA-JAPAN-KOREA JOINT SYMPOSIUM ON OPTIMIZATION OF STRUCTURAL AND MECHANICAL SYSTEMS, 2006, : 419 - 424