arXiv:2606.03038v1 Announce Type: new Abstract: Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate devices when the final decision depends on localized output-port readouts. This risk is acute in propagation-dominated MMI splitters and couplers, where port power, splitting, phase, and coupling are determined by accumulated modal interference and output-window aggregation rather than by average field similarity alone. We study this field-to-design mismatch through a Field/Mediator/Readout view that
The paper highlights a critical challenge in the development and reliability of AI surrogates for photonic design, emerging as these tools become more sophisticated and widely adopted.
This research is important for a strategic reader because it points to the limitations of current AI design methods in a critical technology domain, indicating the need for more nuanced metrics beyond global accuracy.
The focus shifts from general AI model accuracy to the specific and critical problem of 'port readout' accuracy, impacting the development and trust in AI-driven photonic design tools.
- · AI model developers
- · Photonic engineering
- · Semiconductor industry
- · Computational physics
- · Over-reliant AI design processes
- · Inaccurate surrogate models
Improved metrics for evaluating AI models in engineering domains will emerge, leading to more robust and reliable design tools.
This could accelerate the design and fabrication of complex photonic devices, which are crucial for next-generation computing and communication infrastructure.
The enhanced efficiency and accuracy in photonic design could lower costs, democratizing advanced optical technologies and accelerating their integration into new applications.
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Read at arXiv cs.LG