Knowledge Discovery in Nanophotonics Using AI

This research presents a novel paradigm for designing nanostructures and understanding light-matter interactions through explainable AI (XAI) techniques. Conventional approaches struggle with the high-dimensional design space and non-unique input-output relations in nanophotonic structures. To overcome these challenges, we developed dimensionality reduction (DR) techniques to simplify the design process and an inverse design approach to resolve the non-uniqueness problem.

By employing manifold learning, we reduced the complexity of the inverse design problem while preserving key information. This enabled the discovery of optimal nanostructures with minimal geometrical complexity, resulting in innovative methods to accelerate designing all-optical neuro-inspired computing frameworks and nanoscale biosensors.

Related Publications

• Kiarashinejad, Y. and Adibi, A., Georgia Tech Research Corp, 2023. Optical co-processor architecture using array of weak optical perceptron. U.S. Patent Application 17/919,051. Link
• Zandehshahvar, M., Kiarashi, Y., Zhu, M., Bao, D., H Javani, M., Pourabolghasem, R. and Adibi, A., 2023. Metric learning: harnessing the power of machine learning in nanophotonics. Acs Photonics, 10(4), pp.900-909. Link
• Kiarashinejad, Y., Zandehshahvar, M., Abdollahramezani, S., Hemmatyar, O. and Adibi, A., Georgia Tech Research Corp, 2022. Geometric Learning-Based Method for Discovery of Optical Phenomena in Nanophotonic Structures. U.S. Patent Application 17/474,523. Link
• Zandehshahvar, M., Kiarashi, Y., Zhu, M., Maleki, H., Brown, T. and Adibi, A., 2022. Manifold learning for knowledge discovery and intelligent inverse design of photonic nanostructures: breaking the geometric complexity. Acs Photonics, 9(2), pp.714-721. Link
• Kiarashinejad, Y., Abdollahramezani, S. and Adibi, A., Georgia Tech Research Corp, 2022. Systems and Methods for Enhanced Engineering Design and Optimization. U.S. Patent Application 17/294,837. Link
• Zandehshahvar, M., Kiarashi, Y., Chen, M., Barton, R. and Adibi, A., 2021. Inverse design of photonic nanostructures using dimensionality reduction: reducing the computational complexity. Optics Letters, 46(11), pp.2634-2637. Link
• Abdollahramezani, S., Hemmatyar, O., Taghinejad, M., Taghinejad, H., Kiarashinejad, Y., Zandehshahvar, M., Fan, T., Deshmukh, S., Eftekhar, A.A., Cai, W. and Pop, E., 2021. Dynamic hybrid metasurfaces. Nano Letters, 21(3), pp.1238-1245. Link
• Hemmatyar, O., Abdollahramezani, S., Kiarashinejad, Y., Zandehshahvar, M. and Adibi, A., 2019. Full color generation with fano-type resonant hfo 2 nanopillars designed by a deep-learning approach. Nanoscale, 11(44), pp.21266-21274. Link
• Kiarashinejad, Y., Abdollahramezani, S., Zandehshahvar, M., Hemmatyar, O. and Adibi, A., 2019. Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices. Advanced Theory and Simulations, 2(9), p.1900088. Link
• Kiarashinejad, Y., Abdollahramezani, S. and Adibi, A., 2020. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures. npj Computational Materials, 6(1), p.12. Link
• Kiarashinejad, Y., Zandehshahvar, M., Abdollahramezani, S., Hemmatyar, O., Pourabolghasem, R. and Adibi, A., 2020. Knowledge discovery in nanophotonics using geometric deep learning. Advanced Intelligent Systems, 2(2), p.1900132. Link