Transformer-based learned dimensional collapse for silicon photonic inverse design

Aras Arslan; Ahmet Onur Dasdemir; Furkan Aykut Sarikamis; Berna Kiraz; Alper Kiraz; Abdullah Magden; Emir Salih Magden

doi:10.1117/12.3080973

Transformer-based learned dimensional collapse for silicon photonic inverse design

Aras Arslan
, Ahmet Onur Dasdemir
, Furkan Aykut Sarikamis
, Berna Kiraz
, Alper Kiraz
, Abdullah Magden
, Emir Salih Magden^*

^*Bu çalışma için yazışmadan sorumlu yazar

Yapay Zeka ve Veri Mühendisliği Bölümü

Koc University
Center for Image Analysis (OGAM)
Bursa Teknik University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

Özet

We present a deep learning framework that revolutionizes photonic device design by collapsing costly 3D electromagnetic simulations into fast, accurate 2D representations. Our dual-stage, Transformer-based architecture combines a rapid factorization-cached 2D FDFD solver with a U-Transformer network to reconstruct full 3D fields, while a dedicated phase module preserves phase integrity. Trained on 16,000 3D-FDTD simulations of random silicon photonic devices, our model achieves over 99.1% field-matching accuracy and enables inverse design optimizations that are over 100 times faster than traditional methods. Designed devices exhibit less than 0.5 dB transmission mismatch and more than 90% structural similarity to 3D-FDTD results across the 1.5-1.6 µm wavelength range. This scalable approach enables high-throughput, rapid, and practical design workflows for next-generation photonic components.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Physics and Simulation of Optoelectronic Devices XXXIV
Editörler	Marek Osinski, Yasuhiko Arakawa, Frederic Grillot, Frederic Grillot
Yayınlayan	SPIE
ISBN (Elektronik)	9781510696976
DOI'lar	https://doi.org/10.1117/12.3080973
Yayın durumu	Yayınlandı - 5 Mar 2026
Etkinlik	34th Physics and Simulation of Optoelectronic Devices - San Francisco, United States Süre: 19 Oca 2026 → 22 Oca 2026

Yayın serisi

Adı	Proceedings of SPIE - The International Society for Optical Engineering
Hacim	13890
ISSN (Basılı)	0277-786X
ISSN (Elektronik)	1996-756X

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	34th Physics and Simulation of Optoelectronic Devices
Ülke/Bölge	United States
Şehir	San Francisco
Periyot	19/01/26 → 22/01/26

Bibliyografik not

Publisher Copyright:
© 2026 SPIE. All rights reserved.

Belgeye Erişim

10.1117/12.3080973

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Arslan, A., Dasdemir, A. O., Sarikamis, F. A., Kiraz, B., Kiraz, A., Magden, A., & Magden, E. S. (2026). Transformer-based learned dimensional collapse for silicon photonic inverse design. In M. Osinski, Y. Arakawa, F. Grillot, & F. Grillot (Eds.), Physics and Simulation of Optoelectronic Devices XXXIV Makale 1389010 (Proceedings of SPIE - The International Society for Optical Engineering; Hac. 13890). SPIE. https://doi.org/10.1117/12.3080973

Arslan, Aras ; Dasdemir, Ahmet Onur ; Sarikamis, Furkan Aykut et al. / Transformer-based learned dimensional collapse for silicon photonic inverse design. Physics and Simulation of Optoelectronic Devices XXXIV. editör / Marek Osinski ; Yasuhiko Arakawa ; Frederic Grillot ; Frederic Grillot. SPIE, 2026. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{5a0dce5284f84184afe3c424fa2a8d5a,

title = "Transformer-based learned dimensional collapse for silicon photonic inverse design",

abstract = "We present a deep learning framework that revolutionizes photonic device design by collapsing costly 3D electromagnetic simulations into fast, accurate 2D representations. Our dual-stage, Transformer-based architecture combines a rapid factorization-cached 2D FDFD solver with a U-Transformer network to reconstruct full 3D fields, while a dedicated phase module preserves phase integrity. Trained on 16,000 3D-FDTD simulations of random silicon photonic devices, our model achieves over 99.1\% field-matching accuracy and enables inverse design optimizations that are over 100 times faster than traditional methods. Designed devices exhibit less than 0.5 dB transmission mismatch and more than 90\% structural similarity to 3D-FDTD results across the 1.5-1.6 µm wavelength range. This scalable approach enables high-throughput, rapid, and practical design workflows for next-generation photonic components.",

keywords = "Electromagnetic simulations, Inverse-photonics design, Transformers",

author = "Aras Arslan and Dasdemir, \{Ahmet Onur\} and Sarikamis, \{Furkan Aykut\} and Berna Kiraz and Alper Kiraz and Abdullah Magden and Magden, \{Emir Salih\}",

year = "2026",

month = mar,

day = "5",

doi = "10.1117/12.3080973",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Marek Osinski and Yasuhiko Arakawa and Frederic Grillot and Frederic Grillot",

booktitle = "Physics and Simulation of Optoelectronic Devices XXXIV",

address = "United States",

}

Arslan, A, Dasdemir, AO, Sarikamis, FA, Kiraz, B, Kiraz, A, Magden, A & Magden, ES 2026, Transformer-based learned dimensional collapse for silicon photonic inverse design. in M Osinski, Y Arakawa, F Grillot & F Grillot (eds), Physics and Simulation of Optoelectronic Devices XXXIV., 1389010, Proceedings of SPIE - The International Society for Optical Engineering, hac. 13890, SPIE, 34th Physics and Simulation of Optoelectronic Devices, San Francisco, United States, 19/01/26. https://doi.org/10.1117/12.3080973

Transformer-based learned dimensional collapse for silicon photonic inverse design. / Arslan, Aras; Dasdemir, Ahmet Onur; Sarikamis, Furkan Aykut et al.
Physics and Simulation of Optoelectronic Devices XXXIV. ed. / Marek Osinski; Yasuhiko Arakawa; Frederic Grillot; Frederic Grillot. SPIE, 2026. 1389010 (Proceedings of SPIE - The International Society for Optical Engineering; Hac. 13890).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Transformer-based learned dimensional collapse for silicon photonic inverse design

AU - Arslan, Aras

AU - Dasdemir, Ahmet Onur

AU - Sarikamis, Furkan Aykut

AU - Kiraz, Berna

AU - Kiraz, Alper

AU - Magden, Abdullah

AU - Magden, Emir Salih

PY - 2026/3/5

Y1 - 2026/3/5

N2 - We present a deep learning framework that revolutionizes photonic device design by collapsing costly 3D electromagnetic simulations into fast, accurate 2D representations. Our dual-stage, Transformer-based architecture combines a rapid factorization-cached 2D FDFD solver with a U-Transformer network to reconstruct full 3D fields, while a dedicated phase module preserves phase integrity. Trained on 16,000 3D-FDTD simulations of random silicon photonic devices, our model achieves over 99.1% field-matching accuracy and enables inverse design optimizations that are over 100 times faster than traditional methods. Designed devices exhibit less than 0.5 dB transmission mismatch and more than 90% structural similarity to 3D-FDTD results across the 1.5-1.6 µm wavelength range. This scalable approach enables high-throughput, rapid, and practical design workflows for next-generation photonic components.

AB - We present a deep learning framework that revolutionizes photonic device design by collapsing costly 3D electromagnetic simulations into fast, accurate 2D representations. Our dual-stage, Transformer-based architecture combines a rapid factorization-cached 2D FDFD solver with a U-Transformer network to reconstruct full 3D fields, while a dedicated phase module preserves phase integrity. Trained on 16,000 3D-FDTD simulations of random silicon photonic devices, our model achieves over 99.1% field-matching accuracy and enables inverse design optimizations that are over 100 times faster than traditional methods. Designed devices exhibit less than 0.5 dB transmission mismatch and more than 90% structural similarity to 3D-FDTD results across the 1.5-1.6 µm wavelength range. This scalable approach enables high-throughput, rapid, and practical design workflows for next-generation photonic components.

KW - Electromagnetic simulations

KW - Inverse-photonics design

KW - Transformers

UR - https://www.scopus.com/pages/publications/105040048841

U2 - 10.1117/12.3080973

DO - 10.1117/12.3080973

M3 - Conference contribution

AN - SCOPUS:105040048841

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Physics and Simulation of Optoelectronic Devices XXXIV

A2 - Osinski, Marek

A2 - Arakawa, Yasuhiko

A2 - Grillot, Frederic

PB - SPIE

T2 - 34th Physics and Simulation of Optoelectronic Devices

Y2 - 19 January 2026 through 22 January 2026

ER -

Arslan A, Dasdemir AO, Sarikamis FA, Kiraz B, Kiraz A, Magden A et al. Transformer-based learned dimensional collapse for silicon photonic inverse design. In Osinski M, Arakawa Y, Grillot F, Grillot F, editörler, Physics and Simulation of Optoelectronic Devices XXXIV. SPIE. 2026. 1389010. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.3080973

Transformer-based learned dimensional collapse for silicon photonic inverse design

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap