# Programmable metasurfaces for future photonic artificial intelligence

arXiv: https://arxiv.org/abs/2505.11659 · HTML: https://mlsanity.com/paper/2505.11659/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 148 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit](https://mlsanity.com/paper/2008.11659/) (2020)
- [Training of Physical Neural Networks](https://mlsanity.com/paper/2406.03372/) (2024)
- [Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks](https://mlsanity.com/paper/2205.08501/) (2022)
- [Backpropagation-free Training of Deep Physical Neural Networks](https://mlsanity.com/paper/2304.11042/) (2023)
- [The physics of optical computing](https://mlsanity.com/paper/2308.00088/) (2023)
- [Self-learning Machines based on Hamiltonian Echo Backpropagation](https://mlsanity.com/paper/2103.04992/) (2021)
- [Image sensing with multilayer, nonlinear optical neural networks](https://mlsanity.com/paper/2207.14293/) (2022)
- [Nonlinear Processing with Linear Optics](https://mlsanity.com/paper/2307.08533/) (2023)
- [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019)
- [Forward-Forward Training of an Optical Neural Network](https://mlsanity.com/paper/2305.19170/) (2023)
- [An Optical Frontend for a Convolutional Neural Network](https://mlsanity.com/paper/1901.03661/) (2018)
- [Metasurface-Enabled On-Chip Multiplexed Diffractive Neural Networks in the Visible](https://mlsanity.com/paper/2107.07873/) (2021)
- [Nonlinear optical encoding enabled by recurrent linear scattering](https://mlsanity.com/paper/2307.08558/) (2023)
- [Arbitrary control over multimode wave propagation for machine learning](https://mlsanity.com/paper/2402.17750/) (2024)
- [Cascadable all-optical NAND gates using diffractive networks](https://mlsanity.com/paper/2111.01404/) (2021)
- [Photonic Advantage of Optical Encoders](https://mlsanity.com/paper/2305.01743/) (2023)
- [The hardware is the software](https://mlsanity.com/paper/2310.18335/) (2023)
- [On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels](https://mlsanity.com/paper/2302.04993/) (2023)
- [Three dimensional waveguide-interconnects for scalable integration of photonic neural networks](https://mlsanity.com/paper/1912.08203/) (2019)
- [A single inverse-designed photonic structure that performs parallel computing](https://mlsanity.com/paper/2009.01187/) (2020)
- [Comment on "All-optical machine learning using diffractive deep neural networks"](https://mlsanity.com/paper/1809.08360/) (2018)
- [Response to Comment on "All-optical machine learning using diffractive deep neural networks"](https://mlsanity.com/paper/1810.04384/) (2018)
- [The Spatial Complexity of Optical Computing and How to Reduce It](https://mlsanity.com/paper/2411.10435/) (2024)

## Abstract (arXiv)

Photonic neural networks (PNNs), which share the inherent benefits of photonic systems, such as high parallelism and low power consumption, could challenge traditional digital neural networks in terms of energy efficiency, latency, and throughput. However, producing scalable photonic artificial intelligence (AI) solutions remains challenging. To make photonic AI models viable, the scalability problem needs to be solved. Large optical AI models implemented on PNNs are only commercially feasible if the advantages of optical computation outweigh the cost of their input-output overhead. In this Perspective, we discuss how field-programmable metasurface technology may become a key hardware ingredient in achieving scalable photonic AI accelerators and how it can compete with current digital electronic technologies. Programmability or reconfigurability is a pivotal component for PNN hardware, enabling in situ training and accommodating non-stationary use cases that require fine-tuning or transfer learning. Co-integration with electronics, 3D stacking, and large-scale manufacturing of metasurfaces would significantly improve PNN scalability and functionalities. Programmable metasurfaces could address some of the current challenges that PNNs face and enable next-generation photonic AI technology.