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Machine learning enabled the design of compact andefficient wavelength demultiplexing photonic devices

2023 IEEE Photonics Conference (IPC) | 979-8-3503-4722-7/23/$31.00 ©2023 IEEE | DOI: 10.1109/IPC57732.2023.10360715

M. Turduev1, E. Bor2, O. Alparslan3, Y. S. Hanay4, H. Kurt5, S. Arakawa3, and M. Murata3
1. Department of Electrical and Electronics Engineering, Kyrgyz Turkish Manas University, Bishkek 720042, Kyrgyzstan
2. Department of Electrical and Electronics Engineering, TOBB University of Economics and Technology, Ankara 06560, Turkey
3. Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
4. Department of Computer Engineering, Akdeniz University, Antalya 07070, Turkey
5. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
mirbek.turduev@manas.edu.kg

Abstract— In this paper, we introduce the design approach ofintegrated photonic devices by employing reinforcement learning
known as attractor selection (AttSel). Here, we combined 3D
FDTD with AttSel algorithm, which is based on artificial neuralnetworks, to achieve ultra-compact and highly efficient
wavelength demultiplexers with low crosstalk such as. The
presented devices consist of SOI materials, which are compatible
with complementary MOS technology. Consequently, thereinforcement learning is successfully applied to design smallerand superior integrated photonic devices.
Keywords—wavelength demultiplexing, inverse design, machinelearningI.

INTRODUCTION

Traditional photonic device design relies on intuition andmanual adjustment of a few parameters, limiting efficiency.
However, innovative approaches integrating optimization
algorithms and numerical methods have revolutionized thefield, enabling accurate calculations of light-matter interactions[1-3]. Recently, "inverse-design" methods using silicon-oninsulator (SOI) materials have emerged, allowing for the designof ultra-compact, highly efficient, CMOS-compatible
integrated photonic devices. Convex optimization is employed
for wavelength demultiplexer design [4, 5]. Recentdevelopments in machine learning and artificial neuralnetworks have fueled interest among researchers in the designof photonic metasurfaces and Bragg grating devices [6,7].
Furthermore, reinforcement learning techniques have beenutilized to design optical couplers and asymmetric lighttransmitters [8, 9].

Fig. 1. (a) Perspective view of designed device for demultiplexing thewavelengths of 1300 nm, 1550 nm, 1800 nm) with its structuralparameters and (b) plots of normalized transmission efficiencies at theoutput waveguides. Same colors are used for arrays at output
waveguides and corresponding calculated transmission efficiencies.

In this study, we showcase the design of near-infrared
wavelength demultiplexers (WDM) using the attractor
selection (AttSel) algorithm. AttSel is a reinforcement learning
approach based on artificial neural networks [9]. The designed
structures achieve exceptional optical performance within aremarkably
compact
footprint.
Detailed
numerical
investigations of these structures are provided.
II. DESIGN APPROACH AND NUMERICAL INVESTIGATION OF
INTEGRATED PHOTONIC DEVICES

AttSel models the interaction of the metabolic reaction
network and the gene regulatory network in a cell [10]. Cell
growth depends on converting environmental nutrition throughmetabolic protein reactions. Gene expression levels control
protein production, which adjusts based on substanceproduction rates. Favorable conditions create stable attractor
states, while unfavorable conditions prompt the cell to search
for new expression levels. Noise-induced deviations drive

Fig. 2. Calculated magnetic field intesity distributions at the wavelengths of (a) 1300 nm, (b) 1550 nm and (c) 1800 nm for the firstly designed wavelengths
demultiplexer device.
979-8-3503-4722-7/23/$31.00 ©2023 IEEE

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Fig. 3. (a) Perspective view of designed device for demultiplexing thewavelengths of 1500 nm, 1550 nm, 1600 nm) with its structuralparameters and (b) plots of normalized transmission efficiencies at theoutput waveguides. Same colors are used for arrays at output waveguides
and corresponding calculated transmission efficiencies.

exploration until favorable conditions are restored. The
individual cells in the photonic device can be interpreted as theproteins, whose expression levels are represented as =( , , ⋯ , ) . If the expression level of , which is between[-1, 1], is < 0, then the ith individual cell in the photonic
device is defined to be an air. Otherwise, it is defined to be Si.
In this study, by applying AttSel, we designed photonicstructures with sizes of 3×3 µm2 and slab thickness of 300 nm
which are composed of Si material on a SiO2 substrate. The
design area is divided into square pixels with dimensions of
100×100 nm2 where the algorithm determined the states of eachpixel to be Si or air according to the desired optical property.
Here, the refractive indices of Si and air are fixed as nSi=3.46
and nair=1.0, respectively, at near-infrared wavelengths around1550 nm. Input and output waveguides having width of 500 nm
are connected to design area to form a photonic device.
Here, we present the WDM which separates the incident
wavelengths of 1300 nm, 1550 nm and 1800 nm to different
output waveguides. Guided-mode TE0 is considered as incident
light in 3D FDTD simulations. The algorithm increases thetransmission efficiency of a desired wavelength in a selectedwaveguide. The corresponding photonic device is representedin Figure 1(a). The distance between adjacent output
waveguides is 500 nm. The transmission efficiencies arecalculated as 70% (–1.55 dB), 71% (–1.48 dB) and 66% (–1.80dB) at the selected wavelengths of 1300 nm, 1550 nm and 1800
nm, respectively, see Fig. 1(b). Here, there exist negligible levelof crosstalk which is under –12 dB and points out efficient
demultiplexing. In Fig. 2, magnetic field intensity (|Hz|2)
distributions are presented at selected wavelengths. Here, theincident light at wavelength of 1300 nm is directed to the upper

output waveguide whereas wavelength of 1800 nm is canalized
to lower output waveguide. The middle output waveguideconducts the wavelengths at around 1550 nm.
In WDM systems, compactness, channel spacing, number ofchannels, high transmission efficiency and low crosstalk arehighly desirable criteria. For this reason, AttSel is applied tofulfil all the requirements in a WDM device. Here, we designed
another photonic device to separate closely selected
wavelengths of 1500 nm, 1550 nm and 1600 nm withtransmission efficiencies of 75% (–1.25 dB) at different output
waveguides. The designed photonic devices are schematicallyrepresented in Figure 3(a) with its structural parameters. Also,
the corresponding transmission efficiencies are plotted inFigure 3(b). In addition, we extracted the magnetic field
distributions at the selected wavelengths which are presented in
Figure 4. It is clear to see that the proposed device is capable ofdemultiplexing the selected wavelengths with small channel
spacing of 50 nm. The proposed wavelength demultiplexer canseparate three wavelengths between 1500 nm and 1600 nm withchannel spacing of 50 nm. Also, our device has ultra-compact
sizes of 9.0 µm2 and transmission efficiencies –1.25 dB.
Proposed structure ultra-compact footprint and hightransmission efficiencies for a slightly larger channel spacingof 10 nm. Also, it has low crosstalk between waveguides even
if the waveguides are closely placed to each other with 500 nm.
REFERENCES[1] Bor E, Turduev M and Kurt H 2016 Sci. Rep. 6 30781[2] Turduev M, Bor E and Kurt H 2017 J. Phys. D 50 38LT02[3] Bor E, Turduev M and Kurt H 2019 J. Opt. 21 085801[4] Piggott A Y, Lu J, Lagoudakis K G, Petykiewicz J, Babinec T M
and Vuckovic J 2015 Nat. Photonics 9 374[5] Su L, Piggott A Y, Sapra N V, Petykiewicz J and Vuckovic J
2017 ACS Photonics 5 301[6] Nadell C C, Huang B, Malof J M and Padilla W J 2019 Opt.
Express 27 27523.
[7] Gao L, Li X, Liu D, Wang L and Yu Z 2019 Adv. Mater. 311905467[8] Turduev M, Bor E, Latifoglu C, Giden I H, Hanay Y S and Kurt
H 2018 J. Lightwave Technol. 36 2812
[9] Bor E, Alparslan O, Turduev M, Hanay Y S, Kurt H, Arakawa Sand Murata M 2018 Opt. Express 26 29032[10] Kashiwagi A, Urabe I, Kaneko K and Yomo T 2006 PLoS One
1 e49.

Fig. 4. Calculated magnetic field intesity distributions at the wavelengths of (a) 1500 nm, (b) 1550 nm and (c) 1600 nm for the secondly designed wavelengths
demultiplexer device.

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