ENEE 691 Special Topics: Machine Learning and Photonics (Spring 2023)

Linear Regression

• Chugh et al., Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses, Journal of Lightwave Technology, 2019.
• Simsek et al., Photodetector Performance Prediction with Machine Learning, Frontiers in Optics, 2021. Abstract.

ANN

• Chugh et al., Machine learning approach for computing optical properties of a photonic crystal fiber, OpEx, 2019.
• Simsek et al., Designing Photodetectors with Machine Learning, Advanced Photonics Congress, 2022.
• Peurifoy et al., Nanophotonic particle simulation and inverse design using artificial neural networks, Science Advances, 2018.
• Zhang et al., Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks, Photonics Research, 2019.

CNN

• Wiecha and Muskens, Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures, Nano Letters, 2019.

RNN

• Salmela et al., Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network, Nature Machine Intelligence, 2021.

PCA

• Kiarashinejad et al., Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures, Computational Materials, 2020.
• Melati et al.,  Mapping the global design space of nanophotonic components using machine learning pattern recognition, Nature Communications, 2019.

Adjoint Method

• Lalau-Keraly et al., Adjoint shape optimization applied to electromagnetic design, Optical Express, 2013.
• Piggot et al., Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer, Nature Photonics, 2015.
• Hughes et al., Adjoint Method and Inverse Design for Nonlinear Nanophotonic Devices, ACS Photonics, 2018.
• Yeung et al., Enhancing Adjoint Optimization-Based Photonic Inverse Design with Explainable Machine Learning, ACS Photonics, 2022.

Back-to-Back Networks

• Ma, Cheng, and Liu, Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials, ACS Nano, 2018.
• Tahersima et al., Deep Neural Network Inverse Design of Integrated Photonics Power Splitters, Scientific Reports, 2019.
• An et al., A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design, ACS Photonics, 2019.
• Liu et al., Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures, ACS Photonics, 2019.

GANs

• Liu et al., Generative Model for the Inverse Design of Metasurfaces, Nano Letters, 2018.
• Jiang and Fan, Global Optimization of Dielectric Metasurfaces Using a Physics-Driven Neural Network, Nano Letters, 2019.
• Jiang et al, Free-Form Diffractive Metagrating Design Based on Generative Adversarial Networks, ACS Nano, 2019.
• Christensen et al., Predictive and generative machine learning models for photonic crystals, Nanophotonics, 2020.
• Kudyshev et al., Machine learning–assisted global optimization of photonic devices, Nanophotonics, 2021.

In chronological order

• Yao, Unni, and Zheng, Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale, Nanophotonics, 2018.
• Kudyshev, Shalaev, and Boltasseva, Machine Learning for Integrated Quantum Photonics, ACS Photonics, 2020.
• Brown et al., Machine Learning in Nanoscience: Big Data at Small Scales, ACS Nano Letters, 2020.
• Jiang, Chen, and Fan, Deep neural networks for the evaluation and design of photonic devices, Nature Reviews, 2020.
• Ma et al., Deep learning for the design of photonic structures, Nature Photonics, 2020.
• Liu, Raju, and Cai, Tackling Photonic Inverse Design with Machine Learning, Advanced Science, 2021.
• Genty et al., Machine learning and applications in ultrafast photonics, Nature Photonics, 2021.
• Piccinotti et al., Artificial intelligence for photonics and photonic materials, Rep. Prog. Phys., 2021. 

The talk starts at 3m18 and ends around 1h14m.

References (in chronological order)

1. Caulfield and Dolev, Why future supercomputing requires optics, Nature Photonics, 2010.
2. Hamerly et al., Large-Scale Optical Neural Networks Based on Photoelectric Multiplication, PhysRevX 9 2019.
3. Bogaerts et al., Programmable photonic circuits, Nature 586, 2020.
4. Wetzstein et al., Inference in artificial intelligence with deep optics and photonics, Nature 588, 2020.
5. Xu et al., 11 TOPS photonic convolutional accelerator for optical neural networks, Nature 589, 2021.
6. Feldmann et al., Parallel convolutional processing using an integrated photonic tensor core, Nature 589, 2021.
7. Shastri et al., Photonics for artificial intelligence and neuromorphic computing, Nature Photonics 15, 2021.
8. Wang et al., An optical neural network using less than 1 photon per multiplication, Nature Comm 13. 2022.
9. Wright et al., Deep physical neural networks trained with backpropagation, Nature 601, 2022.
10. Cordaro et al., Solving integral equations in free space with inverse-designed ultrathin optical metagratings, Nature Nanotechnology, January 2023.

• Photodetector dataset
• Quantum efficiency dataset
• 1D EM Inversion dataset
• Metanet: Papers with Code
• Fan Group on GitHub

• Prof. Bogaerts: Photonic ICs, Silicon Photonics & Programmable Photonics
• Prof. Shanhui Fan: Neural networks based on optical and wave physics
• Prof. Jelena Vučković: From Inverse Design to Implementation of Practical Quantum Photonics
• Prof. Michal Lipson: Next Generation Silicon Photonics
• Prof. Pleros Nikos, Photonics for Computing: from Optical Interconnects to Neuromorphic Architectures
• Dr. Scott Diddams (from NIST): Fundamentals of frequency combs: What they are and how they work

• UMBC Graduate School’s Policy and Procedures for Student Academic Misconduct
• COVID-19 safety protocols and compliance
• Netiquette

Accessibility and Disability Accommodations, Guidance, and Resources

Accommodations for students with disabilities are provided for all students with a qualified disability under the Americans with Disabilities Act (ADA & ADAAA) and Section 504 of the Rehabilitation Act who request and are eligible for accommodations. The Office of Student Disability Services (SDS) is the UMBC department designated to coordinate accommodations that creates equal access for students when barriers to participation exist in University courses, programs, or activities.

If you have a documented disability and need to request academic accommodations in your courses, please refer to the SDS website at sds.umbc.edu for registration information and office procedures.

SDS email: disAbility@umbc.edu

SDS phone: 410-455-2459

If you will be using SDS approved accommodations in this class, please contact the instructor to discuss implementation of the accommodations. During remote instruction requirements due to COVID, communication and flexibility will be essential for success.

Sexual Assault, Sexual Harassment, and Gender Based Violence and Discrimination 

UMBC Policy and Federal law (Title IX) prohibit discrimination and harassment on the basis of sex, sexual orientation, and gender identity in University programs and activities. Any student who is impacted by sexual harassment, sexual assault, domestic violence, dating violence, stalking, sexual exploitation, gender discrimination, pregnancy discrimination, gender-based harassment or retaliation should contact the University’s Title IX Coordinator to make a report and/or access support and resources:

Tiffany Chen, Interim Title IX Coordinator

410-455-1717, tiffanc2@umbc.edu

You can access support and resources even if you do not want to take any further action. You will not be forced to file a formal complaint or police report. Please be aware that the University may take action on its own if essential to protect the safety of the community.

If you are interested in or thinking about making a report, please use the Online Reporting/Referral Form. Please note that, if you report anonymously,  the University’s ability to respond will be limited.

Notice that Faculty are Responsible Employees with Mandatory Reporting Obligations:

All faculty members are considered Responsible Employees, per UMBC’s Policy on Sexual Misconduct, Sexual Harassment, and Gender Discrimination. Faculty are therefore required to report any/ all available information regarding conduct falling under the Policy and violations of the Policy to the Title IX Coordinator, even if a student discloses an experience that occurred before attending UMBC and/or an incident that only involves people not affiliated with UMBC.  Reports are required regardless of the amount of detail provided and even in instances where support has already been offered or received.

While faculty members want encourage you to share information related to your life experiences through discussion and written work, students should understand that faculty are required to report past and present sexual assault, domestic and interpersonal violence, stalking, and gender discrimination that is shared with them to the Title IX Coordinator so that the University can inform students of their rights, resources and support.  While you are encouraged to do so, you are not obligated to respond to outreach conducted as a result of a report to the Title IX Coordinator.

If you need to speak with someone in confidence, who does not have an obligation to report to the Title IX Coordinator, UMBC has a number of Confidential Resources available to support you: 

• Retriever Integrated Health (Main Campus): 410-455-2472 [Monday – Friday; 8:30 a.m. – 5 p.m.] / After-Hours Support 410-455-3230
• Center for Counseling and Consultation (Shady Grove Campus): 301-738-6273 (Messages checked hourly)  Online Appointment Request Form
• Pastoral Counseling via Interfaith Center: 410-455-3657; interfaith@umbc.edu [7 days a week; Fall and Spring 7 a.m. – 11 p.m.; Summer and Winter 8 a.m. – 8 p.m.]

Other Resources:

• Women’s Center (for students of all genders): 410-455-2714; womenscenter@umbc.edu. [Monday – Thursday 10:00am-5:30pm and Friday 10:00am-4pm]
• Shady Grove Student Resources, Maryland Resources, National Resources.

Child Abuse and Neglect:

Please note that Maryland law and UMBC policy require that faculty report all disclosures or suspicions of child abuse or neglect to the Department of Social Services and/or the police even if the person who experienced the abuse or neglect is now over 18.

Pregnant and Parenting Students

UMBC’s Policy on Sexual Misconduct, Sexual Harassment and Gender Discrimination expressly prohibits all forms of Discrimination and Harassment on the basis of sex, including pregnancy. Resources for pregnant, parenting and breastfeeding students are available through the University’s Office of Equity and Inclusion.  Pregnant and parenting students are encouraged to contact the Title IX Coordinator to discuss plans and ensure ongoing access to their academic program with respect to a leave of absence or return following leave related to pregnancy, delivery, adoption, breastfeeding and/or the early months of parenting.

Pregnant students and students in the early months of parenting may be entitled to accommodations under Title IX through the Office of Equity and Inclusion.

In addition, students who are pregnant and have an impairment related to their pregnancy that qualifies as disability under the ADA may be entitled to accommodations through the Student Disability Service Office.

Religious Observances & Accommodations

UMBC Policy provides that students should not be penalized because of observances of their religious beliefs, and that students shall be given an opportunity, whenever feasible, to make up within a reasonable time any academic assignment that is missed due to individual participation in religious observances. It is the responsibility of the student to inform the instructor of any intended absences or requested modifications for religious observances in advance, and as early as possible. For questions or guidance regarding religious observance accommodations  please contact the Office of Equity and Inclusion at oei@umbc.edu.

Hate, Bias, Discrimination and Harassment

UMBC values safety, cultural and ethnic diversity, social responsibility, lifelong learning, equity, and civic engagement.

Consistent with these principles, UMBC Policy prohibits discrimination and harassment in its educational programs and activities or with respect to employment terms and conditions based on race, creed, color, religion, sex, gender, pregnancy, ancestry, age, gender identity or expression, national origin, veterans status, marital status, sexual orientation, physical or mental disability, or genetic information.

Students (and faculty and staff) who experience discrimination, harassment, hate or bias or who have such matters reported to them should use the online reporting/referral form to report discrimination, hate or bias incidents. You may report incidents that happen to you anonymously. Please note that, if you report anonymously, the University’s ability to respond will be limited.