r/SiliconPhotonics

There's a whole lot of hype about quantum computing, but that is something else entirely. Why is there so little hype about photonic based computing? There's a handful of startups but they are very low key. Even in academia, there's a whole lot of hype about quantum computing but relatively little hype about optics based computation.

Can anyone with any insight into optics/photonics clue me in?

Hey everyone!

I’m starting my MSc in Germany, and I’m planning to specialize in Applied Optics and Photonics.

My course has a few modules related to data science, image processing, AI, scientific computing, etc., which is one of the reasons I’m really interested in the program. During my bachelor's, I worked with python, numpy, pandas, matplotlib and scientific computing, and I’m currently learning MATLAB as well.

I’m interested in machine learning and how it can be applied to photonics, optical systems, scientific imaging, and instrumentation. I’m trying to figure out how far I can take this combination during my MSc rather than treating AI/ML as something completely separate from photonics.

For anyone working/studying in photonics, scientific instrumentation, computer vision, or ML, I’d really appreciate some advice:

• what programming skills should I develop beyond python/numPy/pandas/matplotlib? Is C++, MATLAB, cuda, pyTorch, etc. worth learning?
• what kind of projects would be good for combining photonics/instrumentation with ML? For example, optical image analysis, spectroscopy, sensor data, signal denoising, inverse problems, etc.
• are there particular areas where ML is genuinely being used in industry/research in photonics
• what would you recommend learning/building over the next 1–2 years to become competitive for internships, working-student positions and eventually full-time jobs?

Any advice on what you wish you had learned earlier, good project ideas, research areas, or things I should avoid spending too much time on would be really appreciated!

Thanks!

I am trying to do a sensitivity analysis for a PIC and am interested to know what are the major fabrication errors(i know few common ones like side wall roughness, side wall angles, slab thickness variation) that occur for foundry tape out and how they are modeled in sensitivity analysis (monte carlo?)?

I hope people with experience help me with this.

Now I know that the feedback and reviews for this course have been asked before ample number of times on this subreddit, but I was curious to know if we can also submit active device for this course?

I was primarily thinking of using metal heaters and some components with doping.

I am working on a photonic inverse design project for foundry tapeout but I am curious to know what y'all think about it.

While I understand there are various challenges which come with it from the perspective of yield/fabrication robustness I am curious to what are the major barriers that would prevent people from adopting it commercially.

While in the conventional design approach we can add heaters to help with post fabrication correction which may be challenging with inverse designed devices. But at the same time inverse design can potentially reduce the device footprint?(How important is the footprint issue currently?)

I see plenty of papers on inverse designed Broadband 50:50 couplers but for tapeout, people still stick to the conventional parallel waveguide couples even though the footprint seems bigger.

I also see plenty of Inverse designed WDM, why are they not widely used in say the MRR based transceivers?