Revolutionary Integration of Artificial Intelligence with Meta-Optics-Focus on Metalenses for Imaging
Abstract
:1. Introduction
2. Standard Refractive Lenses, Diffractive Lenses and Metalenses
3. Types of Metalenses
3.1. Dielectric Metalenses
3.2. Plasmonic Metalenses
3.3. Gradient Index (GRIN) Metalenses
3.4. Chiral Metalens
3.5. Parabolic Metalenses
3.6. Hybrid Metalenses
3.7. Reconfigurable Metalenses
3.8. Binocular and Metalens Array
Type of Metalens | Material | Mechanism | Advantages | Disadvantages | Applications |
---|---|---|---|---|---|
Dielectric Metalenses | High-index dielectrics (e.g., TiO2, Si) | Phase control through geometric phase or propagation phase | High efficiency, low loss, compatibility with visible light | Fabrication complexity, requires high precision | Imaging, microscopy, AR/VR devices [66,151] |
Plasmonic Metalenses | Metals (e.g., Au, Ag) | Surface plasmon resonances to control light | Compact, capable of sub-wavelength focusing | High loss, limited efficiency, typically for near-IR or visible | Sensing, imaging, nano-optics [88,152] |
GRIN Metalenses | Gradient refractive index materials | Spatial variation of refractive index [153] | Broad wavelength range, reduced CA | Complex fabrication, limited material choices | Imaging systems, optical communication [154,155] |
Chiral Metalens | Chiral metamaterials | Circular dichroism and birefringence | Polarization control, circular dichroism | Limited efficiency, complex design | Polarization optics, spectroscopy [114,156,157] |
Reflective parabolic metalenses | TiO2, SiO2 | Parabolic phase profile, Bragg reflector | High reflectivity, efficient focusing | Limited to reflective applications | Telescopes, optical communication [124] |
Hybrid Metalenses | Combination of dielectric and plasmonic materials | Integrates benefits of both dielectric and plasmonic mechanisms | Enhanced functionality, improved efficiency and bandwidth | Increased design and fabrication complexity | Multifunctional optical devices, sensing [19,130] |
Reconfigurable Metalenses | Tunable materials (e.g., liquid crystals, phase-change materials) | Dynamically controlled phase shift | Tunable focal length and beam shaping, adaptable to varying conditions | Slow response time, material stability, fabrication challenges | Adaptive optics, dynamic imaging systems [140,158] |
4. Synergy between AI and MOs
4.1. Brief History of AI
- The Initial Stage (1950s–1960s): focused on creating heuristic algorithms and programs designed to solve artistic and intellectual difficulties.
- The Intermediate Stage (Late 1960s–1970s): aimed at developing intelligent robots skilled at modeling the external world, identifying and evaluating situations, making decisions, forming behavioral plans, and engaging in natural language communication.
- The Modern Stage (Mid-1970s–Present): characterized by the enhancement of intelligent human–computer systems that combine human intelligence with computational abilities.
4.2. Role of AI in MOs-Focus on Metalenses
4.3. Examples of Recently Developed AI-Powered Metalenses for Imaging
4.3.1. Monocular Camera
4.3.2. Ultraspectral Imaging
4.3.3. Virtual Reality (VR)
5. Challenges and Future Prospectives
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Abbreviation | Full Form |
MO | Meta-optics |
ML | Machine learning |
DL | Deep learning |
MS | Metasurface |
VR | Virtual reality |
AR | Augmented reality |
EA | Evolutionary algorithm |
GRIN | Graded index |
AI | Artificial intelligence |
TL | Transfer learning |
RL | Reinforcement learning |
BO | Bayesian optimization |
GA | Genetic algorithms |
CA | Chromatic aberration |
SL | Supervised learning |
USL | Unsupervised learning |
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Benefit | Description | AI Techniques Involved |
---|---|---|
Design optimization | AI can optimize the design parameters of meta-optical components to achieve desired properties, such as minimizing aberrations or enhancing resolution. | Machine learning (ML), Genetic algorithms (GAs) [38] |
Inverse design | AI algorithms can assist in the inverse design process, where desired optical responses are specified, and the system computes the necessary meta-structure to achieve these responses. | Deep learning (DL), Neural networks [42] |
Performance prediction | AI can predict the performance of meta-optical devices under various conditions, reducing the need for extensive simulations and experimental trials. | Predictive modeling, Regression analysis [43] |
Material discovery | AI can help discover new materials with specific properties suitable for MOs applications by analyzing large datasets of material properties. | Data mining, Bayesian optimization (BO) [44] |
Fabrication process optimization | AI can optimize the fabrication processes of meta-optical components, improving yield and reducing defects. | Reinforcement learning (RL), Process control algorithms [45] |
Real-time adaptive optics | AI can enable real-time adjustments and corrections in adaptive optical systems, improving image quality and system performance in dynamic environments. | Real-time data processing, Adaptive algorithms [46] |
Pattern recognition and classification | AI can enhance pattern recognition and classification in imaging systems, improving the accuracy of optical sensing and imaging applications. | Computer vision, convolutional neural networks [47] |
Enhanced sensing | AI can enhance the capabilities of meta-optical sensors by improving signal processing and noise reduction, leading to more accurate and reliable measurements. | Signal processing, Noise reduction techniques [48] |
Automated data analysis | AI can automate the analysis of large datasets generated by meta-optical systems, extracting meaningful insights and reducing the need for manual analysis. | Big data analytics, Automated feature extraction [49] |
Virtual prototyping | AI can create virtual prototypes of meta-optical devices, allowing for extensive testing and optimization before physical prototypes are built. | Simulation, Virtual reality techniques [50] |
Performance prediction | AI can predict the performance of meta-optical devices under various conditions, reducing the need for extensive simulations and experimental trials. | Predictive modeling, Regression analysis [51] |
Characteristic | Refractive Lens | Diffractive Lens | Metalens |
---|---|---|---|
Working mechanism | Bending of light rays through refraction | Diffraction and interference | Manipulation of light using nanostructures |
Material | Typically glass or plastic | Typically plastic or glass with microstructures | Dielectrics or metals with nanoscale structures |
Thickness | Generally thick, especially for high power | Can be very thin | Ultra-thin (sub-wavelength scale) |
Weight | Relatively heavy | Lighter compared to refractive lenses | Extremely lightweight |
Aberrations | Susceptible to CA | High CA but can be designed to minimize it | Can be engineered to correct aberrations, including chromatic |
Manufacturing Complexity | Moderate to high | High due to precise microstructure fabrication | Very high due to nanoscale fabrication requirements |
Focal Length Variation | Fixed for a given lens shape | Fixed for a given structure | Can be dynamically varied using external stimuli (e.g., voltage) |
Applications | Common in cameras, eyeglasses, microscopes | Used in diffractive optical elements (DOE), holography | Advanced imaging systems, compact optical devices, AR |
Efficiency | High | Moderate (efficiency can decrease with diffraction orders) | High efficiency can be achieved, but depends on design and material |
Scalability | Scalable, but size affects weight and thickness | Scalable with microfabrication techniques | Highly scalable with advanced nanofabrication techniques |
Wavelength Dependency | Less wavelength dependent (broadband) | Strong wavelength dependence | Can be designed for specific wavelengths or broadband operation |
Technique | Definition | Key Characteristics | Applications |
---|---|---|---|
ML | Algorithms that learn patterns from data | SL, USL, and semi-supervised learning. Focus on predictive accuracy. Requires labeled data for SL | Image and speech recognition, predictive analytics, natural language processing |
DL | Subset of ML using NNs | Hierarchical learning architecture. Automated feature extraction. State-of-the-art performance in many domains. Requires large amounts of data and computational resources | Computer vision, natural language processing, robotics |
EAs | Optimization techniques inspired by natural selection | Population-based optimization. Uses genetic operators (mutation, crossover). Suitable for complex, nonlinear problems | Engineering design, financial modeling, game-playing strategies |
RL | Learning through interaction with an environment | Agent learns by trial and error. Maximizes cumulative reward. Markov Decision Processes (MDPs) are often used as a framework | Game playing (e.g., AlphaGo), robotics, Autonomous driving |
BO | Optimization technique based on BO | Uses prior information and observations to guide the search. Efficient for expensive, black-box functions. Balances exploration and exploitation | Hyperparameter tuning, experimental design, robotics motion planning |
TL | Leveraging knowledge from one domain for another | Transfers knowledge learned from one task to improve learning in another task. Reduces the need for large amounts of labeled data in new domains | Image classification, natural language understanding, medical diagnosis |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Kazanskiy, N.L.; Khonina, S.N.; Oseledets, I.V.; Nikonorov, A.V.; Butt, M.A. Revolutionary Integration of Artificial Intelligence with Meta-Optics-Focus on Metalenses for Imaging. Technologies 2024, 12, 143. https://doi.org/10.3390/technologies12090143
Kazanskiy NL, Khonina SN, Oseledets IV, Nikonorov AV, Butt MA. Revolutionary Integration of Artificial Intelligence with Meta-Optics-Focus on Metalenses for Imaging. Technologies. 2024; 12(9):143. https://doi.org/10.3390/technologies12090143
Chicago/Turabian StyleKazanskiy, Nikolay L., Svetlana N. Khonina, Ivan V. Oseledets, Artem V. Nikonorov, and Muhammad A. Butt. 2024. "Revolutionary Integration of Artificial Intelligence with Meta-Optics-Focus on Metalenses for Imaging" Technologies 12, no. 9: 143. https://doi.org/10.3390/technologies12090143