Report
Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
| العنوان: | Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks |
|---|---|
| المؤلفون: | Luo, Yi, Mengu, Deniz, Yardimci, Nezih T., Rivenson, Yair, Veli, Muhammed, Jarrahi, Mona, Ozcan, Aydogan |
| المصدر: | Light: Science & Applications (2019) |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Neural and Evolutionary Computing, Physics - Computational Physics, Physics - Optics |
| الوصف: | We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tunable, single passband as well as dual passband spectral filters, and (2) spatially-controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep learning-based design strategy, broadband diffractive neural networks help us engineer light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. Comment: 36 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1038/s41377-019-0223-1 |
| URL الوصول: | http://arxiv.org/abs/1909.06553 |
| رقم الانضمام: | edsarx.1909.06553 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1038/s41377-019-0223-1 |
|---|