The fields of optics and photonics have always stood at the forefront of technological innovation. From early lenses and telescopes to sophisticated laser systems and fiber optics, these disciplines demand the highest levels of technical precision and control, as well as complex modeling. As AI and machine learning transform industry, their profound influence on optics and photonics is growing, too. The integration of AI into the design and manufacturing of optical components, for example, is unlocking unprecedented efficiency and enabling previously unattainable performance. Even in this early era of AI integration, leading institutions and companies are adopting these technologies in ways that are groundbreaking and signal broader implications for the future. AI and photonics: The synergy AI and machine learning technologies excel at recognizing patterns, making predictions, and optimizing complex systems — all of which are essential in photonics. Fabricating components such as lenses, mirrors, and waveguides, as well as integrated photonics systems, requires designers to work with large data sets, consider nonlinear behaviors, and impose stringent performance requirements. AI helps engineers to analyze this data more effectively. Further, it helps them simulate performance under various conditions and automate the discovery of optical configurations. Similarly, engineers can deploy machine learning algorithms trained on simulation, experimental, and production data to model optical system behavior with remarkable accuracy. This accelerates design cycles. At the same time, it reduces costs and improves component performance; machine learning is used to handle high-dimensional data and identify subtle correlations often missed by traditional methods. Regression analysis and classification are among the techniques that are increasing in popularity among end users. Neural networking — specifically, convolutional neural networks (CNNs) for image analysis and recurrent neural networks for sequential data — is also gaining traction for predicting optical properties, identifying defects, and optimizing manufacturing parameters. Today, five distinct application areas exemplify the synergy between AI/ machine learning and essential optics and photonics tasks (see sidebar on page 60). Functions range from real-time inspections and process monitoring to data analysis and recovery. Laser beam characterization TRUMPF, a global leader in laser technology and industrial machine tools, is deploying machine learning to enhance laser beam characterization, a foundational aspect of optics manufacturing. Beam quality is crucial for precision lasers applications, including cutting and welding, additive manufacturing, and semiconductor lithography. Maintaining consistent and precise beam properties is paramount to process reliability and product quality. Traditionally, beam characterization approaches involve manual calibration and analysis, which can be slow and prone to errors. Moreover, beam characterization via these methods often requires skilled operators and can lead to costly periods of downtime if issues are not detected early in-process. TRUMPF has integrated machine learning into diagnostic systems that automatically assess beam parameters such as shape, focus position, and power distribution in real time (Figure 1). TRUMPF’s neural networks are trained on historical beam data to detect anomalies, recommend adjustments, and predict failures before they occur. Figure 1. TRUMPF’s machine-learning-driven laser beam characterization mechanism. Wavefront correction occurs in ~150 ms. Courtesy of TRUMPF. In addition to reducing downtime and improving quality, this machine learning deployment also enables tighter tolerances. This transition from reactive maintenance to predictive maintenance is a major benefit, as it ensures consistent output and minimizes waste. Optimization in optical equipment CCTT Optech, a Montréal-based center of expertise affiliated with Cégep de La Pocatière in Québec, is pioneering the use of AI in optical equipment development. A key initiative involves optimizing imaging systems for industrial inspection. By applying machine learning to massive data sets from high-speed cameras and sensors, engineers fine-tune lighting, focus, and image processing parameters, resulting in highly accurate inspections. CNNs are particularly well suited for analyzing image data to identify defects or features automatically. Often, they surpass human capabilities in speed and consistency. In another project, CCTT Optech developed a retinal camera that features an innovative LED lighting system and smartphone integration. AI is used to assess image quality, improve acquisition, and reduce the workload for ophthalmologists. Early tests showed only 8% of images were classified as “good,” and 12% as “usable,” while 74% were rejected (Figure 2). After optimization, these metrics improved dramatically: 74% were classified as “good,” 80% “usable,” and only 12% were rejected. In this case, the AI supports better diagnostic workflows and potentially increased access to screening. Figure 2. CCTT Optech’s retinal camera, featuring LED ring lighting and smartphone integration capabilities to yield high precision results. Courtesy of CCTT Optech. CCTT Optech is also exploring reinforcement learning to develop adaptive optical systems that respond dynamically to environmental or target changes. Reinforcement learning allows systems to learn optimal control strategies through trial and error. These systems can then dynamically adjust optical parameters such as focus or aberration correction in response to changing conditions, without requiring explicit programming for every distinct scenario. This initiative, therefore, has the potential to revolutionize automated inspection, autonomous vehicles, and remote sensing. Automating optical design Designing high-performance systems typically requires balancing dozens or even hundreds of variables, such as curvature, thickness, and refractive index. Traditional optical design often uses iterative optimization algorithms that can become stuck in local minima and require significant human expertise to redirect and guide the process. Researchers at the Centre for Optics, Photonics and Lasers (COPL) at Laval University in Québec City are using AI to streamline and enhance optical design. Instead of relying only on traditional ray tracing and iterative optimization, the researchers are using machine learning models to identify optimal design parameters based on desired outputs (Figure 3). This automation approach considerably reduces computational overhead and design time. Techniques such as genetic algorithms and neural networks are being used in parallel to explore the vast design space more efficiently and discover nonintuitive solutions. Figure 3. Researchers at the Laval University-based Centre for Optics, Photonics and Lasers (COPL) are using AI to streamline and enhance optical design (right). The image shows an example of the output produced by the researchers’ AI assistant lens designer. The tool can output thousands of optical designs for a particular application. Courtesy of COPL. The Laval researchers are also advancing inverse design techniques, in which a desired optical function is specified first, and AI algorithms then “invent” the structure needed to achieve it. These methods have enabled the researchers to develop compact, high-performance components for photonic integrated circuits (PICs). The inverse technique represents a paradigm shift from the concept behind forward design, in which a structure is designed and its function then simulated. In the inverse technique, AI explores potential structures to fulfill a predefined function. This approach is particularly powerful for complex nanophotonic structures, where intuition based on traditional optics breaks down. High-resolution wavefront sensing Wavefront sensing is critical for high-precision applications, including adaptive optics, microscopy, and laser processing. Imagine Optic, a leader in this field, is using AI to push the limits of wavefront sensing. Its sensors, powered by a machine learning algorithm called local iterative fitting technique (LIFT), detect ultrafine aberrations in optical wavefronts. The company’s standard HASO SWIR sensors deliver 28 × 28 resolution. Incorporating LIFT, resolution increases fourfold to 112 × 112. This significant increase makes it possible for users to detect and then correct for finer details in the wavefront. The final result is improved quality for imaging applications or improved laser performance (Figure 4). Figure 4. Imagine Optic’s use of AI pushes the limits of wavefront sensing. An image of a human retina (top images) acquired via optical coherence tomography before (top left) and after (top right) AI image denoising. AI phase reconstruction (bottom right) from a Hartmanngram raw signal (bottom left). Courtesy of Top images of the complete graphic are courtesy of Imagine Eyes and CEA. Full image courtesy of Imagine Optic. AI-driven analysis enhances both the sensitivity and speed of wavefront evaluation. These systems filter noise, identify subtle patterns, and suggest real-time corrections, all of which are vital for dynamic imaging and high-throughput environments. Processing large amounts of sensor data quickly and accurately, via AI, is invaluable for real-time adaptive optics systems used in applications such as high-resolution microscopy and free-space optical communications. Enhancing optical testing and automation The German firm ficonTEC Service specializes in automated assembly and testing of photonic devices, functions that are critical for scaling up the production of complex PICs and other optical components. FiconTEC’s AI-driven robotic systems achieve submicron alignment accuracy using computer vision technology to detect alignment errors, assess quality, and optimize process parameters (Figure 5). Current iterations of the company’s AI systems have “learned” to self-correct, and they are continuously improving. Machine vision, often powered by deep learning, is used to guide robotic arms with high precision and perform automated visual defect inspections. Figure 5. The layout of the ficonEDGE edge computing platform for improving machine key performance indicators. Data is streamed from the machine to the edge device and automatically evaluated by machine learning algorithms. Two pathways are used to improve performance, and self-adaptive production uses model outputs directly on the machine to improve performance without human intervention. Two interfaces, for alerting and analysis, trigger local technical staff to perform maintenance actions or identify bottlenecks for optimization. Courtesy of ficonTEC. Additionally, ficonTEC’s AI-enabled platforms simulate environmental and operational conditions to ensure product reliability for high-volume production. Predictive modeling using AI helps to identify potential failure points and optimize manufacturing processes to improve the yield and reliability of photonic devices. AI-driven photonics market intelligence Netherlands-based software developer and solutions provider SCITODATE takes an altogether different approach to the application of AI, using its solution to bridge science and market opportunity. The company’s platform uses AI and expert systems to analyze vast volumes of scientific literature, patents, and market data, helping organizations to identify emerging trends in optics and photonics. These insights assist businesses in identifying white spaces, anticipating demand, and strategically directing R&D efforts. For example, in the energy sector — where optics plays a key role in sensing and laser-based processing — SCITODATE’s AI solution helps to reveal untapped applications for photonics technologies. By analyzing the language and trends in scientific publications and patent filings, AI is also used to identify connections and potential applications that might not be immediately obvious through manual review. Toward a smarter photonics era AI and machine learning are not just tools: They are catalysts for redefining the possibilities in optical engineering. From reducing development cycles to enabling real-time adaptive systems, these technologies are reshaping how optical components are designed, tested, and manufactured. While challenges remain — including data requirements, model interpretability, and integration with systems — ongoing innovation is steadily addressing these barriers. Looking ahead, AI will penetrate even deeper into the optical value chain. Generative AI will enable the creation of entirely new optical architectures, potentially leading to the discovery of novel optical phenomena and devices with unprecedented properties. And quantum machine learning could soon converge with quantum optics to open a frontier of capabilities beyond classical limits and revolutionize quantum computing and communication. The implications extend to edge AI, which is poised to bring intelligent optical processing closer to the data source by supporting improved real-time optical sensing and decision-making in autonomous systems, wearables, and remote devices. In digital twins, the creation of virtual replicas of optical systems or manufacturing processes powered by AI will allow for highly accurate simulations, predictive maintenance, and process optimization in a virtual environment. This step must take place before changes can be implemented in the physical world. It is apparent from this forecasted growth that demand for professionals who understand both photonics and data science will continue to grow, driving the emergence of a hybrid discipline. These professionals will be tasked with meeting other challenges that must be considered. They include the computational resources required for training complex models, the need for high-quality and diverse data sets, and the ethics of deploying AI in critical applications. As these technologies evolve, we are already witnessing the dawn of an era in which optics becomes not only more powerful but also profoundly more intelligent. The integration of AI and machine learning is not just an enhancement; it is a fundamental shift that will drive the next wave of innovation in optics and photonics. AI and Machine Learning: Broad-Based Photonics Applications 1. Design and simulation: Accelerating the exploration of parameter spaces, enabling inverse design, and optimizing complex multicomponent systems. 2. Manufacturing and fabrication: Real-time process monitoring, predictive maintenance, quality control, and robot-automated inspection. 3. Characterization and testing: Automated analysis of optical properties, defect detection, and high-throughput metrology. 4. System control and optimization: Adaptive optics, intelligent feedback loops, and self-calibrating systems. 5. Data analysis and discovery: Identifying trends in large experimental or simulation data sets and accelerating the discovery of materials and/or phenomena.
