# Characterizing Coherent Integrated Photonic Neural Networks under Imperfections

arXiv: https://arxiv.org/abs/2207.10835 · HTML: https://mlsanity.com/paper/2207.10835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [A Survey of Deep Learning for Scientific Discovery](https://mlsanity.com/paper/2003.11755/) (2020)
- [A Survey on Silicon Photonics for Deep Learning](https://mlsanity.com/paper/2101.01751/) (2021)
- [Design of optical neural networks with component imprecisions](https://mlsanity.com/paper/2001.01681/) (2019)
- [Modeling Silicon-Photonic Neural Networks under Uncertainties](https://mlsanity.com/paper/2012.10594/) (2020)
- [Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis](https://mlsanity.com/paper/2112.07485/) (2021)
- [CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks](https://mlsanity.com/paper/2112.06098/) (2021)

## Built on by (most cited first, 4 of 4)

- [Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks](https://mlsanity.com/paper/2308.03249/) (2023)
- [The Impact of Feature Representation on the Accuracy of Photonic Neural Networks](https://mlsanity.com/paper/2406.18757/) (2024)
- [MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar](https://mlsanity.com/paper/2607.26016/) (2026)
- [LCAP: Population-Informed Latent Chip Adaptation from Few Output Probes for Photonic Neural Networks](https://mlsanity.com/paper/2609.16823/) (2026)

## Abstract (arXiv)

Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing speed and energy efficiency. In particular, coherent IPNNs use arrays of Mach-Zehnder interferometers (MZIs) for unitary transformations to perform energy-efficient matrix-vector multiplication. However, the underlying MZI devices in IPNNs are susceptible to uncertainties stemming from optical lithographic variations and thermal crosstalk and can experience imprecisions due to non-uniform MZI insertion loss and quantization errors due to low-precision encoding in the tuned phase angles. In this paper, we, for the first time, systematically characterize the impact of such uncertainties and imprecisions (together referred to as imperfections) in IPNNs using a bottom-up approach. We show that their impact on IPNN accuracy can vary widely based on the tuned parameters (e.g., phase angles) of the affected components, their physical location, and the nature and distribution of the imperfections. To improve reliability measures, we identify critical IPNN building blocks that, under imperfections, can lead to catastrophic degradation in the classification accuracy. We show that under multiple simultaneous imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even when the imperfection parameters are restricted within a small range. Our results also indicate that the inferencing accuracy is sensitive to imperfections affecting the MZIs in the linear layers next to the input layer of the IPNN.