Solving the SWaP-C challenge with AI-driven optics

Core4ce details the new advances in artificial intelligence opening another path for designing and building cutting-edge materials.

Autonomous underwater vehicle patrolling ocean with navy ship providing support in the background, ensuring maritime security.
COURTESY: Adobe Stock #1276673285/Michael

Defense and intelligence missions require greater speed, agility, and scalability than ever before. But optic and sensor development hasn’t kept pace as a critical technology for defense operations. Traditional exquisite optical systems are often too expensive, slow to develop, and bulky to easily integrate into the lightweight attritable platforms that’ll be used for upcoming operations across the aerospace, naval, and surveillance domains. The result is a tradeoff between size, weight, power, and cost (SWaP-C), where you change one optimization for another, which has slowed innovation at a time when defense demands are only increasing.

Now, new advances in artificial intelligence (AI) are opening another path for designing and building cutting-edge materials. By using AI to model and simulate millions of optical designs in seconds, engineers can create thinner, lighter, and more scalable materials reshaping what’s possible for defense use.

Legacy optics holding back innovation

What was once cutting-edge, legacy optics have become a bottleneck. Traditional lenses are large, rigid, and power-intensive, requiring platforms to compromise between performance, payload, and cost. In space, every kilogram of weight matters. In aerial platforms, every watt of power can extend or shorten a drone’s mission time. In ground and maritime operations, bulkier sensors limit mobility and increase maintenance demands.

This tradeoff has slowed the ability to field more sensors across more environments. Scaling sensing capabilities for distributed operations is one of the top priorities across modern defense strategies, but it becomes prohibitively expensive when each additional sensor significantly adds to cost, logistics, and complexity.

Another important limitation of traditional optics is their inability to evolve quickly with mission requirements. The production of precision glass or crystal optics involves long lead times, specialized labor, and costly infrastructure. If operational needs shift, as they often do in dynamic environments, defense programs can find themselves stuck with outdated technology that can’t be easily upgraded or modified. This lag restricts innovation at a time when adaptability is essential.

AI, metalenses unlock next-gen sensors

AI is accelerating the development of metalenses for optics, an emerging field where thin, flat, nanoscale structures can manipulate light just as effectively as traditional curved lenses. AI algorithms can quickly simulate millions of metalens variations, identifying the best configurations based on mission-specific criteria such as wavelength coverage, durability, and power efficiency.

The result is faster design cycles, reduced iterations, and optics manufactured at scale. Instead of labor-intensive glass shaping and polishing, meta-surfaces can be produced using methods similar to semiconductor manufacturing, making them far easier and less expensive to mass-produce.

Scientists and engineers can evaluate millions of design permutations in parallel. AI-based optimization tools can prototype lens designs meeting multiple mission criteria simultaneously, without the need for costly or time-consuming guesswork. These tools can account for environmental parameters, manufacturing constraints, and system-level requirements from the outset, producing validated designs that are technically robust and operationally viable.

These thin, lightweight metalenses can also unlock entirely new design possibilities. Disposable drones, microsatellites, and unmanned underwater vehicles can benefit from agile, high-performance sensors not compromising payload or endurance. Space-based platforms, where launch costs are closely tied to mass, could also deploy broader constellations at lower costs, improving coverage and resilience.

Equally important, AI-driven design reduces the trial-and-error traditionally required to create multi-functional optics. Metalenses can be engineered to perform multiple roles – such as imaging, sensing, and targeting – within a single surface. This multi-functionality supports the growing defense emphasis on compact, multi-mission platforms that can adapt to diverse operational needs with minimal hardware changes. It also enables greater flexibility at the tactical edge, where operators benefit from systems that are lighter, simpler, and capable of performing a broader range of tasks.

Manufacturing at scale to meet mission needs

Soldiers using drone for scouting during military operation in the desert.
COURTESY: Adobe Stock #164038449/Gorodenkoff

One of the most important shifts happening is how these AI-designed optics match the broader defense move toward modular, scalable systems. Metalenses can be integrated into the modular architecture, making it easier to upgrade or adapt sensing capabilities without redesigning entire platforms.

This level of manufacturing flexibility will be essential in the coming years. Defense organizations increasingly need to adapt faster than traditional acquisition cycles allow. Systems that can be built, modified, and deployed quickly, without sacrificing performance, will be key to maintaining an advantage in contested environments.

Manufacturability at scale also improves operational resilience. With the ability to produce sensors faster and at lower cost, defense forces can afford to lose, replace, and re-deploy sensing platforms without the same logistical or financial loss as today’s heavier, more expensive systems.

A new era for defense sensing

Overcoming the SWaP-C barrier is about unlocking new operational concepts, not just technical improvements. When sensing systems become lighter, more affordable, and easier to scale, they enable a broader, more resilient approach to modern defense challenges. Defense forces can now field more sensors, maintain persistent coverage, and respond quicker to emerging threats.

AI-driven metalenses mark a fundamental shift in how sensing technologies are designed, manufactured, and deployed. Moving beyond the limitations of legacy materials and processes, defense organizations can now create systems matching the speed, agility, and modularity required for today’s operations. As AI modeling and advanced manufacturing techniques continue to mature, the gap between innovative design and real-world deployment will shrink, helping deliver smarter, faster, and more adaptable solutions for the future of defense.

About the author: Rick Hubbard is the chief engineer at Core4ce’s Autonomy, Artificial Intelligence, and Machine Learning (AAIM) Lab. He leads technical innovation efforts focused on AI-enabled design for optical systems, advanced materials, and autonomous defense technologies.

Core4ce
https://www.core4ce.com

June 2025
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