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An optical neural network (ONN) is a neural network in which optical hardware, rather than only electronic circuits, carries out some of the weighted calculations in its layers. The light does the heavy lifting for specific operations, usually matrix multiplication, while electronics typically handle the rest. ONNs are a research-stage and specialized technology, not a general replacement for the processors that run AI workloads today.
What an optical neural network is
An artificial neural network represents knowledge as weighted connections between units. Each neuron combines its inputs using those weights, applies a nonlinear activation function, and passes the result to the next layer. An optical neural network implements some of those weighted combinations with optical systems that manipulate light as it travels through the hardware.
The word “optical” describes how the computation is carried out. It does not describe a different kind of learning. An ONN can be trained toward the same objectives as any other neural network; the difference lies in the physical medium that performs part of the arithmetic. A network whose data merely travels over a fiber-optic cable is a conventional electronic network with optical communication, and it is not an ONN.
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Most ONN designs follow the same basic sequence, although the physical details vary by architecture:
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- Encode the input onto light. Input values are mapped onto properties of the optical field, such as amplitude, phase, polarization, or the wavelength of separate channels in wavelength-division multiplexing.
- Apply the weights optically. As the field propagates through interferometer meshes, microring-resonator weight banks, attenuator arrays, 4f optical systems, or diffractive optical elements, the optical structure performs a matrix transformation. Trained parameters are mapped onto the physical settings of these elements. In some diffractive designs the parameters are encoded differently, and the connections between layers can be fixed.
- Convert the result back to electronic signals. Photodetectors turn the optical output into electrical values.
- Finish the layer electronically when needed. Nonlinear activation, data storage, control logic, and parameter handling are commonly done in electronics, because implementing nonlinearity efficiently in optical hardware remains a design challenge.
The main appeal is parallelism. Optics can process many signals through the same physical structure at once, which is why the approach has been studied for dense connectivity. A 1987 Optica abstract by Demetri Psaltis, “Optical Neural Computers,” put the motivation simply: “With optics it is feasible to realize the dense connectivity that is evident in neural networks.”
Architecture families
A 2024 review divides ONNs into two broad families: non-integrated systems built from volume (bulk) optical components, and integrated systems built from on-chip optical components. The distinction affects almost every trade-off that follows.
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| Family | Typical examples | Reconfigurability | Main constraints named in the sources |
|---|---|---|---|
| Non-integrated (free-space) | 4f optical systems, diffractive optical elements, other bulk optics | Varies. Some diffractive designs use fixed inter-layer connections or trained structures rather than adjustable weights. | Discrete optical elements that may require careful alignment; larger physical footprint than on-chip designs. |
| Integrated (on-chip photonics) | Mach–Zehnder interferometer meshes, microring-resonator weight banks, WDM-based arrangements | Programmable optical components can adjust weights. | Waveguide count, thermal effects, optical losses, and the electronic-optical conversion interfaces can limit scaling. |
When comparing architectures, use these axes rather than a single “fastest” ranking: integration and footprint, reconfigurability, nonlinearity, scale and input dimensions, and end-to-end system cost. The last one matters most. A fast optical operation can be offset by lasers, modulators, detectors, electronic control, and data movement between optical and electronic domains.
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- Hybrid optoelectronic ONN: optics performs the weighted operations while electronics handle conversion, storage, control, or nonlinear steps. This is the common form of current demonstrations.
- All-optical ONN: the full inference pipeline runs in optics. The 2024 review states that ONNs generally could not independently complete inference without electronic computing hardware, so an end-to-end all-optical system is not established as a general capability. A 2025 commentary reports progress toward more complete optical pipelines but says these systems remain far from electronic accelerators in scale or configurability.
Advantages and their limits
The 2024 review describes low latency, high bandwidth, low power consumption, and parallel signal processing as potential advantages of optical computing. These are potential benefits, and they are workload-specific. Figures reported for individual optical components or demonstrations describe those components or demonstrations under their own conditions. They do not describe complete system performance, and they should not be read as a general advantage over electronic accelerators.
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The same review identifies open challenges in computational density, nonlinearity, scalability, reconfigurability, and practical applications. It also says that practical application scenarios remained rare in its 2024 account, and that ONNs were not as widely applied as their electronic counterparts.
Examples and industry context
The 2024 review lists several research demonstrations: optical processing for imaging and sensing, nonlinear compensation in submarine fiber-optic communication links, and photonic deep-learning inference on edge devices. Each is a specific demonstration rather than evidence of broad commercial deployment.
The same review names Lightmatter and its Envise and Passage products as examples of industry activity in optical computing. That naming reflects the company’s activity as of the 2024 publication. Product availability changes quickly, so check the vendor’s current materials before assuming any product is offered today.
Is optical computing replacing GPUs?
No. Based on the evidence available through 2025, ONNs are a specialized research area with narrow demonstrations, usually run as hybrid systems alongside electronics. Graphics processors and other electronic accelerators remain the practical platform for neural network workloads, and the optical approach is best understood as a possible complement for particular operations, not a replacement.
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In short, an optical neural network is a neural network whose weighted operations are carried out in optical hardware, with the rest of the system usually handled electronically.
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