Why Optical Computing Still Isn’t Mainstream (Yet)?

By Owais AliReviewed by Lexie CornerMay 19 2025

Optical computing uses light for data processing and transmission, offering faster speeds and lower energy consumption than conventional electronic computing. But if the benefits are so clear, why hasn’t it gone mainstream?

Image Credit: Proxima Studio/Shutterstock.com

What Is Optical Computing?

Optical computing uses photons (particles of light) to perform digital computations, transmit data, and store information. It takes advantage of light’s physical properties, such as high speed, parallelism, and low transmission loss, to achieve greater bandwidth and data throughput than traditional electronic computing.

Unlike traditional computing architectures that rely on electrical currents flowing through transistors, optical computing performs logic operations using optical waveguides, nonlinear materials, optical modulators, and resonators. These materials facilitate all-optical switching by enabling one light signal to control another, effectively replicating the function of electronic transistors.

Since photons do not interact as strongly as electrons, optical computing systems can achieve dense parallel processing with reduced heat generation and minimal electromagnetic interference. This makes optical computing a strong candidate for high-performance, energy-efficient computing.¹

The Roadblocks to Widespread Adoption of Optical Computing

Despite its potential, optical computing still faces several barriers to broader adoption. Some of these limitations are attributable to the field’s early stage of development and may be resolved through continued research and innovation.

Others are tied to fundamental limitations of working with light, which require either trade-offs with existing electronic systems or major technological breakthroughs.

Volatile Optical Large-Scale Memory

Developing and integrating all-optical memory remains a significant challenge in optical computing, primarily because current nonlinear optical components lack the necessary integration density and efficiency for practical implementation.

Techniques like wavelength division multiplexing show promise in small-scale applications but do not scale well for more complex computing tasks. Volatile optical memory also tends to have very short retention times, often in the nanosecond range, requiring constant refreshing and adding complexity to system design.

Despite decades of research, efforts to match the density of electronic memory using optical nonlinearities have not yet succeeded. This remains a key bottleneck for optical computing. Resetting memory quickly and reliably is another major challenge. Physical replacement is not practical, while chemical or thermal resets are too slow, limiting the scalability of virtual memory. Mechanical addressing introduces additional complexity, often forcing trade-offs between reliability and storage capacity.

Although Write Once Read Many (WORM) technologies, such as 5D storage, offer substantial capacity gains, their application as volatile memory demands architectural innovations to efficiently manage frequent write operations.

Interconnects for Memory Domains

A key challenge in optical computing is integrating multiple optical memory types within a single system. Current technology lacks the means to seamlessly connect disparate memory domains, often requiring heterogeneous architectures similar to chiplet-based designs in electronics.

As systems separate instruction memory, data memory, and processing units—each potentially relying on different optical mechanisms—interconnect latency and bandwidth become critical constraints. Achieving near-data processing while avoiding von Neumann bottlenecks requires low-latency, high-throughput optical interconnects that maintain signal integrity across diverse photonic domains.

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Physical Design Automation

Physical design automation for optical circuits involves computational complexity exceeding factorial growth relative to component count, becoming infeasible beyond about 10 components or 100 links.

This challenge requires advanced heuristics, approximation algorithms, and statistical methods. Unlike electronic design automation, optical circuit design must incorporate timing and power considerations early in the process, significantly impacting component placement and routing decisions.

Conventional electronic design tools are inadequate due to fundamental differences in optical signal propagation and timing constraints, necessitating the integration of artificial intelligence techniques to efficiently explore the extensive solution space.²

Integration with Current Infrastructure

Integration with existing electronic infrastructure poses substantial compatibility challenges for optical computing systems. Signal conversion between optical and electronic domains introduces latency and power overhead at interface points, diminishing the inherent speed advantages of optical processing.

The lack of standardized physical connection protocols between optical and electronic components leads to interoperability issues among different manufacturers and technologies.

Due to distinct heat dissipation characteristics, power delivery systems designed for electronic devices must be redesigned to meet the specific demands of optical components like modulators and lasers.

Furthermore, communication protocols optimized for electronic data transfer must be adapted to accommodate optical signal properties, adding complexity to system architecture and limiting seamless integration with current infrastructure.

Diminishing Returns at the Component Level

While optical systems offer clear advantages in bandwidth at the system level, those benefits are less pronounced at the scale of individual components such as logic gates or switches.

For instance, modern electronic transistors exhibit delays on the order of ~1 ps, limiting the potential speed advantage of optical components to approximately 1,000×—considerably lower than the frequently cited 100,000× improvement.

This smaller margin makes it harder to justify replacing mature, highly optimized electronic logic with optical alternatives, especially given the added complexity, cost, and size of optical components.

Production Costs and Commercial Scalability

High production costs and limited scalability remain key economic barriers to the broader adoption of optical computing. Fabricating optical components requires greater precision than typical electronic parts, often demanding expensive equipment and tightly controlled environments.

The supply chain for optical hardware is still underdeveloped, lacking the economies of scale that help reduce costs in the semiconductor industry. Testing and quality assurance also depend on specialized tools and expertise that are not widely available in standard manufacturing settings.

System integration adds further cost, as it requires precise alignment of components from multiple vendors. As a result, the economic benefits of optical computing are unlikely to be realized until production volumes increase enough to offset the upfront investment and learning curve.^3-5

Looking Ahead

Research in optical computing is advancing rapidly, with ongoing efforts focused on resolving longstanding integration and performance challenges.

For example, the Photonic Arithmetic Computing Engine (Pace), developed by Lightelligence in Singapore, integrates over 16,000 photonic components and demonstrates the scalability of optical processors for practical applications. This architecture effectively bridges the photonic–electronic hardware divide while maintaining computational fidelity.⁶

Similarly, Lightmatter’s photonic processor has demonstrated capabilities across natural language processing and reinforcement learning tasks, including text generation, sentiment analysis, and gameplay in classic video games, achieving performance comparable to conventional electronic processors.⁷

Despite these advances, optical computing has yet to achieve a pivotal breakthrough enabling widespread commercial deployment. Progress will likely come through hybrid architectures that combine optical and electronic elements, easing adoption and delivering incremental gains in performance and energy efficiency.

As transistor miniaturization reaches physical limits and thermal inefficiencies increase, optical computing presents a promising path forward. Continued innovation in materials, design tools, and photonic integration will be essential to unlock its full potential.

To explore related advances, see:

• Advances in Photonic Devices for Optical Computing
• Optical Computing Breakthrough with Photonic Processor
• Harnessing Quantum Computing for Breakthroughs in Artificial Intelligence
• Improving Quantum Computer Error Correction with Deep Learning
• Optical Networks Get a Boost with Quantum-Powered Routing Solution

References and Further Reading

1. Touch, J., Badawy, A. H., & Sorger, V. J. (2017). Optical computing. Nanophotonics, 6(3), 503-505. https://doi.org//10.1515/nanoph-2016-0185
2. Kissner, M., & Del Bino, L. (2023, March). Challenges in digital optical computing. In Optical Interconnects XXIII (Vol. 12427, p. 1242702). SPIE. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12427/1242702/Challenges-in-digital-optical-computing/10.1117/12.2649011.full
3. Findlight. (2019). Optical Computing: Prospects and Challenges. [Online]. https://www.findlight.net/blog/optical-computing/
4. Kazanskiy, N. L., Butt, M. A., & Khonina, S. N. (2022). Optical Computing: Status and Perspectives. Nanomaterials (Basel, Switzerland), 12(13), 2171. https://doi.org/10.3390/nano12132171
5. McMahon, P. L. (2023). The physics of optical computing. Nature Reviews Physics, 5(12), 717-734. https://doi.org/10.1038/s42254-023-00645-5
6. Hua, S., Divita, E., Yu, S., Peng, B., Su, Z., Chen, Z., Bai, Y., Zou, J., Zhu, Y., Xu, Y., Lu, C., Di, Y., Chen, H., Jiang, L., Wang, L., Ou, L., Zhang, C., Chen, J., Zhang, W., . . . Shen, Y. (2025). An integrated large-scale photonic accelerator with ultralow latency. Nature, 640(8058), 361-367. https://doi.org/10.1038/s41586-025-08786-6
7. Ahmed, S. R., Baghdadi, R., Bernadskiy, M., Bowman, N., Braid, R., Carr, J., Chen, C., Ciccarella, P., Cole, M., Cooke, J., Desai, K., Dorta, C., Elmhurst, J., Gardiner, B., Greenwald, E., Gupta, S., Husbands, P., Jones, B., Kopa, A., . . . Harris, N. C. (2025). Universal photonic artificial intelligence acceleration. Nature, 640(8058), 368-374. https://doi.org/10.1038/s41586-025-08854-x

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