Abstract
The rapid evolution and complexity of optical communication systems demand continuous advancements in key components, particularly bandpass filters, to ensure efficient signal transmission and reception. Plasmonic bandpass filters, leveraging surface plasmon polaritons (SPPs), exhibit great potential in achieving precise spectral filtering and compact integration. In this comprehensive review, we explore recent strides in plasmonic bandpass filter design and optimization, emphasizing their crucial role in enhancing optical communication systems. Relevant databases such as Scopus, Web of Science, and Google Scholar were utilized to gather and analyze pertinent research articles. The inherent properties of plasmonic materials, facilitating subwavelength-scale light manipulation, make plasmonic bandpass filters attractive for optical communication applications. Control over transmission and rejection bands is vital for optimizing optical channels and minimizing crosstalk in networks. However, challenges such as material losses, fabrication complexity, and integration constraints persist. The review encompasses in-depth discussions on material choices, geometric design strategies, and optimization techniques in plasmonic filter design. Advancements in fabrication methods, including nanolithography and chemical synthesis, have significantly refined filter production. The emergence of machine learning-assisted design and nonlinear plasmonic effects showcases exciting future prospects. In conclusion, while challenges remain, the future of plasmonic bandpass filters is promising. Emerging trends such as nanophotonic integration, active tunability, and metasurface-based designs are reshaping the landscape. Overcoming current obstacles will propel plasmonic bandpass filters toward a future where they play a pivotal role in enabling high-capacity and adaptable optical communication systems.
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1 Introduction
The rapid growth and evolving complexity of modern optical communication systems, driven by escalating data demands and technological advancements, necessitate continuous enhancement of key components to ensure efficient signal transmission and reception. Among these critical components, bandpass filters play a pivotal role in managing the spectral properties of optical signals. These filters enable the isolation of specific wavelengths, crucial for high-capacity data transmission within optical networks. In recent years, plasmonic bandpass filters have emerged as highly promising candidates for achieving precise spectral filtering and compact integration within optical communication systems. Leveraging the unique properties of surface plasmon polaritons (SPPs), which are collective electron oscillations occurring at the metal–dielectric interface, plasmonic bandpass filters offer unprecedented capabilities to manipulate light at subwavelength scales. This characteristic holds immense potential for revolutionizing optical communication applications by providing superior control over transmission and rejection bands [1].
The focus of this comprehensive review is to delve into the recent advancements in the design and optimization of plasmonic bandpass filters, shedding light on their pivotal role in augmenting optical communication systems. We will explore fundamental principles, advanced design strategies, optimization techniques, material considerations, fabrication methodologies, and a wide array of applications. By comprehensively analyzing these aspects, we aim to provide valuable insights into the latest advancements in plasmonic bandpass filter technology and their transformative potential in modern optical communication. The field of plasmonics and its application in bandpass filter design for optical communication systems have seen a surge of interest and research in recent years. Several significant studies have paved the way for understanding the underlying principles and optimizing the performance of plasmonic bandpass filters.
Early research [2] elucidated the principles of plasmon resonances and their potential in subwavelength optical components. This foundational work sparked subsequent research exploring the integration of plasmonics into bandpass filters for optical applications. Further, the study [3] highlighted the crucial role of plasmonic materials in achieving strong field confinement and manipulation of electromagnetic waves, setting the stage for the design and optimization of plasmonic bandpass filters. Advancements in computational modeling and simulation techniques have significantly contributed to the optimization of plasmonic bandpass filters. Notable contributions by [4] demonstrated the use of numerical simulations to optimize plasmonic filter designs, emphasizing the importance of geometry and material selection in achieving desired spectral characteristics.
The integration of plasmonic bandpass filters within optical communication systems has been a subject of active research. Recent studies by [5] showcased the potential of plasmonic bandpass filters in enhancing signal-to-noise ratios and reducing noise in dense wavelength division multiplexing (DWDM) systems, highlighting their significance in modern optical networks.
In this review, we explore the fundamentals of plasmonic bandpass filters, encompassing their working principles, design strategies, optimization techniques, choice of materials, fabrication methods, and applications within optical communication systems. By critically evaluating these aspects, we aim to provide valuable insights into the recent advancements in plasmonic bandpass filter technology and shed light on the challenges and future prospects of this exciting field.
So, this paper is organized as follows. Section two discusses the methodology adopted for the presented review. Section three provides a discussion about designing and fabrication of plasmonic bandpass filters. Optical communication applications of plasmonic bandpass filters are brought up in Sect. 4. Section 5 deals with the existing challenges of the filters, while Sect. 6 presents the future aspects of the plasmonic bandpass filters. Finally, section seven concludes the paper.
1.1 Core concepts and contents
1.1.1 Fundamental principles of plasmonic bandpass filters
Plasmonic bandpass filters operate on the principle of manipulating surface plasmon polaritons (SPPs) to achieve selective wavelength transmission. The unique ability of plasmonic materials to support collective electron oscillations at metal-dielectric interfaces underpins the operation of these filters. The review begins by elucidating the basic physical principles governing plasmonic phenomena, including the interaction of light with free electrons in metals, the formation of SPPs, and their dispersion relations. Understanding these principles is crucial for grasping the design strategies and performance characteristics of plasmonic bandpass filters.
1.1.2 Design strategies
The design of plasmonic bandpass filters involves meticulous selection of materials, optimization of geometric parameters, and integration into optical systems.
Material Selection: Discussing the dielectric constants, losses, and resonance characteristics of commonly used plasmonic materials such as gold, silver, aluminum, and novel materials like graphene and transition metal dichalcogenides.
Geometric Optimization: Analyzing the impact of nanostructure dimensions, shapes (e.g., slits, holes, gratings), and arrangements on the spectral response, bandwidth, and resonance properties of the filters.
Integration Considerations: Examining the challenges and solutions in integrating plasmonic filters with other optical components like waveguides and modulators for seamless operation within optical communication systems.
1.1.3 Optimization techniques
To achieve optimal performance, plasmonic bandpass filters require fine-tuning of various parameters. The review highlights:
Numerical Optimization Algorithms: Utilizing Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Simulated Annealing (SA) for refining filter designs.
Multi-Objective Optimization: Addressing conflicting design goals such as maximizing transmission efficiency while minimizing bandwidth.
Hybrid Approaches: Combining different optimization techniques to enhance convergence speed and solution quality.
1.1.4 Materials and fabrication techniques
We delve into the properties of plasmonic materials and the state-of-the-art fabrication techniques essential for realizing high-performance filters:
Plasmonic Materials: Detailed discussion on metals, high-index dielectrics, and composite materials, highlighting their plasmonic properties and suitability for different applications.
Fabrication Methods: Covering nanolithography, nanoimprint lithography, photolithography, chemical synthesis, self-assembly, electrochemical deposition, and atomic layer deposition, with a focus on their precision, scalability, and suitability for creating complex nanostructures.
1.1.5 Applications in optical communication
Plasmonic bandpass filters are pivotal in various optical communication applications. The review covers:
Wavelength Division Multiplexing (WDM): The role of plasmonic filters in selectively isolating and transmitting specific wavelengths.
Signal Enhancement: How these filters enhance optical signal quality by reducing noise and crosstalk.
Optical Signal Processing: Applications in filtering, reshaping signals, and performing all-optical logic operations.
Integrated Photonic Circuits: The potential for miniaturized photonic circuits integrating plasmonic filters with other optical components.
Biophotonic Sensing: Applications in biosensors for real-time, label-free detection of biomolecules.
1.1.6 Challenges
Despite their potential, several challenges impede the widespread adoption of plasmonic bandpass filters. The review identifies and discusses:
High Optical Losses: Strategies to mitigate losses in plasmonic materials to improve filter efficiency.
Fabrication Complexity: Advances in fabrication techniques to achieve precise and cost-effective production.
Sensitivity to Geometry: Addressing the impact of fabrication imperfections on filter performance.
Integration Issues: Overcoming challenges in integrating plasmonic filters with conventional optical components.
Dynamic Control and Tunability: Developing filters with real-time spectral response control.
2 A discussion over plasmonic bandpass filters
2.1 Design strategies for plasmonic bandpass filters
Plasmonic bandpass filters are integral components in optical communication systems, providing a means to selectively transmit certain wavelengths while attenuating others. The design of these filters involves a careful consideration of various factors, including the choice of plasmonic materials, geometric parameters, and the integration of these filters into optical systems.
One fundamental aspect in the design of plasmonic bandpass filters is the careful selection of plasmonic materials. The choice of materials significantly impacts the performance and characteristics of the filter. Articles such as that [6] emphasize the importance of selecting appropriate plasmonic materials based on their dielectric constants and losses to achieve the desired spectral characteristics. Different materials have distinct dispersion relations for surface plasmon polaritons (SPPs), which influence the filter's resonant frequencies and bandwidth. Geometric design parameters play a crucial role in determining the filter's performance. The dimensions and shape of the nanostructures, such as slits, holes, or gratings, are critical in achieving the desired spectral response. A study [7] highlights the significance of geometry in plasmonic devices, showcasing that variations in size and shape drastically influence the resonance properties and bandwidth of the filter.
Integration considerations are also vital for effective filter design. Proper integration of plasmonic bandpass filters with other optical components is necessary for seamless incorporation into optical communication systems. Articles like [8] discuss the integration of plasmonic devices into waveguides and couplers, elucidating how integration affects the overall performance and efficiency of the filter within the optical system. Additionally, optimization techniques are employed to fine-tune the design parameters and improve the filter’s performance. Optimization algorithms, such as genetic algorithms or simulated annealing, are utilized to achieve optimal geometric configurations and spectral responses. An article [9] illustrates the use of particle swarm optimization (PSO) in optimizing plasmonic nanostructure designs to achieve desired transmission characteristics.
The advancement of computational modeling and simulation tools has significantly enhanced the design process. Numerical simulations, such as finite-difference time-domain (FDTD) methods and rigorous coupled wave analysis (RCWA), enable accurate predictions of the filter's behavior and aid in iteratively refining the design. Studies like [10] showcase the utilization of numerical simulations in optimizing the design parameters to achieve specific spectral responses.
The design strategies for plasmonic bandpass filters encompass careful material selection, geometric design optimization, thoughtful integration, and the utilization of advanced computational tools. These strategies collectively contribute to achieving the desired spectral characteristics and optimizing the performance of plasmonic bandpass filters for enhanced optical communication systems.
2.2 Optimization techniques for plasmonic bandpass filters
Optimization plays a crucial role in tailoring plasmonic bandpass filters to meet specific performance criteria. These filters often require fine-tuning of various parameters to achieve desired spectral responses, bandwidths, and transmission efficiencies. Optimization techniques provide the means to efficiently navigate the design space and maximize the performance of plasmonic bandpass filters.
One common approach to optimization is employing numerical algorithms that iteratively refine the filter's parameters to meet predefined objectives. Particle Swarm Optimization (PSO) is one such algorithm used to optimize plasmonic nanostructure designs. In their study, [11] they employed PSO to achieve a desired transmission peak within the bandpass filter, demonstrating the effectiveness of evolutionary algorithms in optimizing plasmonic filter designs. Genetic Algorithms (GAs) represent another powerful optimization tool. GAs mimics the process of natural selection and evolution to search for optimal solutions. In the context of plasmonic bandpass filters, GAs have been used to optimize the geometrical parameters of nanostructures to achieve specific spectral responses. [12] applied GAs to optimize the size and arrangement of nanoslits in a metal film to achieve narrow transmission bands, showcasing the potential of GAs in filter optimization.
Simulated Annealing (SA) is yet another optimization technique that has found applications in optimizing plasmonic filter designs. By simulating the annealing process in metallurgy, SA iteratively refines the filter parameters to minimize an objective function, such as maximizing transmission or minimizing bandwidth. The study [13] utilized SA to optimize the dimensions of nanoslits in metal films, resulting in improved filter performance. Furthermore, Multi-Objective Optimization (MOO) is essential when dealing with conflicting objectives, such as maximizing transmission while minimizing bandwidth. MOO techniques aim to find a set of optimal solutions representing trade-offs between conflicting goals. In the context of plasmonic bandpass filters, MOO is crucial for achieving balanced filter performance. A study [14] used MOO to optimize the geometric parameters of nanoslits for improved transmission and bandwidth characteristics.
Hybrid optimization approaches, combining multiple algorithms, are gaining popularity for plasmonic filter design. These approaches harness the strengths of different algorithms to improve convergence speed and solution quality. For instance, combining PSO with gradient-based optimization methods has shown promise in optimizing complex plasmonic nanostructures. A study [15] demonstrated the effectiveness of a hybrid PSO and quasi-Newton method for optimizing the geometry of nanoslits in metal films, resulting in enhanced filter performance.
Optimization techniques, such as PSO, GAs, SA, MOO, and hybrid approaches, are essential tools for achieving optimal performance in plasmonic bandpass filters. These techniques enable the efficient exploration of the design space, leading to tailored filter designs with improved spectral characteristics and enhanced efficiency.
2.3 Materials and fabrication techniques
2.3.1 Plasmonic materials and their properties
Plasmonic materials are at the heart of plasmonics, allowing the manipulation of light at subwavelength scales due to their unique properties related to surface plasmon polaritons (SPPs). These materials play a pivotal role in the design and functionality of plasmonic bandpass filters, influencing their spectral response, transmission characteristics, and overall performance within optical communication systems.
Metals, such as gold, silver, aluminum, and copper, are commonly used plasmonic materials due to their excellent ability to support and sustain surface plasmons. These materials have a high density of free electrons, making them conducive to collective oscillations and the formation of SPPs. Among these, gold and silver are particularly popular due to their superior plasmonic properties. For example, gold nanoparticles exhibit strong plasmon resonance in the visible range [16]. The choice of metal influences the plasmon resonance wavelength, which is a crucial parameter for bandpass filter design. Dielectric materials can also exhibit plasmonic properties at optical frequencies, known as high-index dielectric nanoparticles. Materials like silicon, germanium, titanium dioxide, and gallium nitride can support Mie-type resonances, analogous to plasmon resonances in metals. These materials have gained attention for their low losses and potential for applications in all-dielectric plasmonics [17]. The use of dielectric materials expands the options for tailoring the spectral response and efficiency of plasmonic bandpass filters.
Composite materials, consisting of both metallic and dielectric components, offer unique opportunities in plasmonic applications. Hybrid structures combining metals with dielectrics can be engineered to synergize the advantages of both material types, achieving enhanced plasmonic properties and reduced losses. For example, a study [18] demonstrated the use of metal-dielectric core–shell nanoparticles to combine the plasmonic properties of metals with the low-loss characteristics of dielectrics, resulting in improved performance. Continued research has explored alternative plasmonic materials, such as graphene and two-dimensional (2D) materials (e.g., transition metal dichalcogenides), which offer unique advantages. Graphene, a single layer of carbon atoms, possesses tunable plasmonic properties and low losses, making it an attractive material for plasmonics [19]. Additionally, 2D materials exhibit intriguing plasmonic behaviors due to quantum confinement effects, providing new avenues for plasmonic bandpass filter design [20].
2.3.2 Fabrication methods for plasmonic bandpass filters
The successful fabrication of plasmonic bandpass filters is crucial to their integration and functionality in optical communication systems. Fabrication techniques must achieve precise control over nanostructure geometry and dimensions, ensuring the desired spectral response and transmission characteristics.
Nanolithography, a key fabrication approach, enables precise patterning of nanostructures on a substrate. Techniques like electron beam lithography (EBL) and focused ion beam (FIB) lithography provide high resolution and control over nanopatterns [21]. EBL, for instance, allows for direct-write patterning at the nanoscale, making it a valuable tool for creating plasmonic nanostructures with specific geometries [22]. Nanoimprint lithography is a versatile technique that involves creating patterns by mechanically pressing a mold into a resist material. It offers high throughput and resolution, making it suitable for large-area patterning of plasmonic nanostructures [23]. NIL has been employed to fabricate various plasmonic structures, including nanohole arrays and nanodisks, with high precision [24]. Photolithography is a well-established technique for creating micron- and submicron-scale patterns on substrates. Coupled with photochemical processes, it allows for the controlled fabrication of plasmonic nanostructures [25]. Photochemical approaches, such as photochemical metal deposition and photochemical etching, are used to form plasmonic nanostructures with high reproducibility and precision.
Chemical synthesis methods, such as chemical vapor deposition (CVD) and sputtering, are employed to deposit plasmonic materials onto substrates [26]. These techniques are advantageous for large-area deposition and are commonly used in the fabrication of plasmonic thin films or nanoparticles. Self-assembly methods utilize molecular interactions to guide the arrangement of nanoparticles, providing a bottom-up approach for creating periodic plasmonic structures [27]. Electrochemical deposition involves using an electric current to deposit plasmonic materials onto a conductive substrate [28]. This technique allows for precise control over nanostructure growth, making it suitable for creating plasmonic nanoarrays and thin films with specific geometries and properties. Atomic layer deposition is a highly controlled and layer-by-layer deposition technique, enabling precise coating of plasmonic materials [29]. ALD is advantageous for conformal coating of complex three-dimensional nanostructures, making it useful in creating plasmonic coatings with controlled thickness and composition.
2.3.3 Advancements in fabrication techniques for plasmonic bandpass filters
The rapid advancements in fabrication techniques have significantly propelled the development of plasmonic bandpass filters, enabling precise control over nanostructure geometries and enhancing the performance of optical communication systems.
Nanolithography techniques, such as electron beam lithography (EBL) and focused ion beam (FIB) lithography, have seen substantial advancements. Modern EBL systems now achieve sub-10-nm resolution, enabling the creation of intricate plasmonic nanostructures [30]. Advanced patterning algorithms and proximity effect correction methods have further improved the precision and throughput of EBL [31]. Similarly, FIB lithography has seen improvements in beam control and patterning speed, facilitating rapid prototyping and fabrication of plasmonic structures [32]. Advancements in 3D nanofabrication techniques have revolutionized the design and functionality of plasmonic bandpass filters. Three-dimensional plasmonic nanostructures offer enhanced light-matter interactions and improved filter performance. Techniques like two-photon lithography and grayscale lithography have emerged to fabricate complex 3D plasmonic structures with high resolution [33]. These advancements have opened new avenues for designing intricate plasmonic filters with tailored spectral responses.
Self-assembly methods have witnessed remarkable progress, allowing for the spontaneous organization of plasmonic nanoparticles into desired configurations. DNA-directed self-assembly, for instance, enables precise control over nanoparticle arrangement and interparticle distances, facilitating the creation of highly ordered plasmonic arrays [34]. Bottom-up self-assembly approaches offer a promising route for scalable and cost-effective fabrication of plasmonic bandpass filters. Bottom-up fabrication approaches, such as colloidal lithography and block copolymer lithography, have seen notable advancements. Colloidal lithography, in combination with novel materials and methods, allows for the creation of large-area, uniform plasmonic nanostructures with enhanced performance [35]. Block copolymer lithography, on the other hand, has evolved to achieve precise control over nanoscale patterns, enabling the fabrication of high-density plasmonic arrays [36].
Deposition techniques, such as atomic layer deposition (ALD) and molecular beam epitaxy (MBE), have been refined to achieve precise control over material properties and thickness. ALD, with its atomic-scale control, has been employed to coat plasmonic materials onto complex nanostructures, improving their functionality and efficiency [37]. MBE has seen advancements in the growth of epitaxial plasmonic films, enabling the creation of high-quality plasmonic devices [38]. A comprehensive summary of plasmonic materials and their characteristics can be found in Table 1. For a comprehensive rundown of plasmonic bandpass filter fabrication techniques, refer to Table 2. An analysis that compares plasmonic and non-plasmonic optical filters is given in Table 3 and Table 4.
3 Applications of plasmonic bandpass filters in optical communication
Wavelength Division Multiplexing (WDM) is a key technology in optical communication, enabling the transmission of multiple signals over a single optical fiber by using different wavelengths (Fig. 1). Plasmonic bandpass filters play a critical role in WDM systems by precisely selecting and isolating specific wavelengths for transmission and reception [45]. These filters help demultiplex the composite signal into individual channels, allowing for efficient data transmission and vastly increasing the data-carrying capacity of optical fibers.
Wavelength division multiplexing (WDM)
Plasmonic bandpass filters aid in enhancing the quality of optical signals by selectively transmitting the desired wavelength band while suppressing noise and unwanted wavelengths. Through precise control over transmission and rejection bands, plasmonic filters assist in reducing noise and crosstalk, improving the signal-to-noise ratio, and enhancing the overall signal quality in optical communication [46]. This is particularly crucial in long-haul communication where signal degradation is a significant concern.
Plasmonic bandpass filters find applications in optical signal processing, including filtering and reshaping optical signals. By carefully designing the spectral response of the filter, it's possible to tailor the filter's passband to specific communication channels, facilitating efficient signal filtering and reshaping [47]. Moreover, plasmonic filters can be employed for all-optical signal regeneration and logic operations, contributing to signal processing capabilities in optical networks. Integration of plasmonic bandpass filters into integrated photonic circuits offers a compact and versatile platform for optical communication. Plasmonic filters can be seamlessly integrated with other optical components, such as waveguides and modulators, to create miniaturized photonic circuits [48]. This integration enhances the overall system efficiency and facilitates the realization of complex functionalities in a compact footprint.
In addition to traditional optical communication, plasmonic bandpass filters find applications in biophotonic sensing and detection (Fig. 2). These filters can be employed in biosensors for label-free detection of biomolecules [49]. Plasmonic sensors utilize the specific spectral responses of the filter to detect minute changes in refractive index, enabling highly sensitive and real-time biosensing for medical and environmental applications. Plasmonic bandpass filters play a crucial role in various applications within optical communication systems, including WDM systems, signal enhancement, optical signal processing, integrated photonic circuits, and biophotonic sensing. Their unique spectral control capabilities and integration potential make them indispensable in advancing the efficiency and performance of modern optical communication technologies.
Use of plasmonic bandpass filters in biosensors
4 Challenges in plasmonic bandpass filter design and implementation
Despite the promising capabilities of plasmonic bandpass filters, several challenges persist in their design and practical implementation, hindering their seamless integration and widespread adoption in optical communication systems.
Plasmonic materials, especially metals like gold and silver, exhibit high optical losses at optical frequencies due to their intrinsic and extrinsic properties [50]. These losses often result in a short propagation length of surface plasmon polaritons (SPPs), limiting the distance over which the plasmonic signals can be effectively transmitted and detected. The losses also reduce the overall filter efficiency and signal-to-noise ratio. Fabrication of plasmonic nanostructures with the required precision and uniformity is a non-trivial task. Advanced nanolithography techniques can achieve the necessary resolutions, but they are often complex, expensive, and time-consuming [51]. This complexity and cost present significant challenges for mass production and large-scale implementation of plasmonic bandpass filters.
Plasmonic bandpass filter performance is highly sensitive to the geometry and material properties of the nanostructures. Even minor deviations or imperfections in geometry during fabrication can lead to significant shifts in the filter's resonance wavelength and bandwidth [52]. Achieving precise control over these parameters is challenging, especially for complex 3D plasmonic structures. The resonance peaks in plasmonic bandpass filters can suffer from broadening due to various factors, including fabrication imperfections, material impurities, and temperature variations [53]. This broadening reduces the selectivity and effectiveness of the filter, impacting its ability to isolate specific wavelengths effectively.
Seamless integration of plasmonic bandpass filters with other optical components, such as waveguides, modulators, and detectors, remains a challenge [54]. The different materials, fabrication processes, and compatibility issues between plasmonic and conventional optical components pose integration challenges, limiting the overall system efficiency and performance. Achieving dynamic control and tunability of plasmonic bandpass filters is a significant challenge. Real-time control of the filter's spectral response or bandwidth remains elusive, hindering their adaptability to changing communication requirements or dynamically varying signal conditions [55]. Addressing these challenges is essential to harness the full potential of plasmonic bandpass filters in optical communication systems. Ongoing research and technological advancements are focused on mitigating these challenges to enable the widespread application of plasmonic filters for enhanced optical communication.
5 Emerging trends and future prospects in plasmonic bandpass filters
Plasmonic bandpass filters have garnered significant attention for their potential to revolutionize optical communication systems and beyond. Several emerging trends and future prospects are shaping the landscape of plasmonic filters, paving the way for innovative applications and enhanced performance.
An emerging trend is the integration of plasmonic bandpass filters into hybrid nanophotonic platforms (Fig. 3). This integration allows combining the strengths of different materials and structures to optimize filter performance [56]. Hybrid platforms, utilizing both plasmonics and dielectrics, enable improved efficiency, reduced losses, and enhanced tunability, extending the application possibilities of plasmonic filters. Active and tunable plasmonic bandpass filters are gaining attention for their adaptability to changing signal conditions and dynamic control. Incorporating active materials, such as phase change materials or liquid crystals, allows for reconfigurable filter responses [57]. These dynamically tunable filters hold promise for real-time optical signal processing and flexible wavelength routing in optical networks.
Schematic diagram of SPR sensor for copper-dielectric-graphene hybrid nanostructures [58]
Metasurfaces, composed of subwavelength nanostructures, are emerging as a versatile platform for designing plasmonic bandpass filters [59]. Metasurface-based filters offer unique phase and amplitude modulation capabilities, enabling precise control over filter responses (Fig. 4). This trend presents exciting opportunities for achieving ultracompact and efficient plasmonic filters. The integration of machine learning and artificial intelligence in the design and optimization of plasmonic filters is an evolving trend [60]. Machine learning algorithms can assist in exploring complex design spaces, optimizing filter geometries, and predicting optimal parameters for desired spectral responses. This trend has the potential to significantly accelerate the design process and improve filter performance.
Metasurfaces: Subwavelength nanostructure [63]
Recent research has demonstrated the versatility and efficacy of metasurface-based filters. Abdelraouf et al. (2022), for instance, presented a high-performance metasurface filter that has good phase control capabilities and a wide tuning range [61]. A metasurface filter design that attained high efficiency and selectivity in the intended frequency range was also reported by Brown & Saavedra (2024) [62]. These studies highlight how metasurfaces' sophisticated control over electromagnetic waves has the potential to completely transform filter design.
Exploration of nonlinear plasmonic effects in bandpass filters is an emerging research area [64]. Nonlinear phenomena, such as second-harmonic generation and four-wave mixing, can be harnessed within plasmonic filters for signal processing, frequency conversion, and generation of new optical frequencies. Nonlinear plasmonic filters offer prospects for enabling advanced functionalities in optical communication. Efforts are being directed towards designing broadband and multiband plasmonic bandpass filters to address the increasing demand for high-speed data transmission. These filters aim to simultaneously accommodate multiple communication channels over a broad spectral range [65]. Advancements in achieving selective transmission in multiple bands are vital for future optical communication systems. Emerging trends and future prospects in plasmonic bandpass filters are focused on integration, tunability, metasurface-based designs, machine learning-assisted optimization, leveraging nonlinear effects, and accommodating broader spectral ranges. In addition to these advancements, recent research has demonstrated significant progress in enhancing the spectral resolution of plasmonic bandpass filters through the use of buried slab waveguides [66, 67]. Structures incorporating buried slab waveguides enable the design of filters with a few 10nm full width at half maximum (FWHM), leading to improved spectral performance.
6 Conclusion
The burgeoning field of plasmonic bandpass filters holds immense promise for the evolution of optical communication systems and related technologies. This comprehensive review has delved into various critical aspects of plasmonic bandpass filter design and implementation, shedding light on advancements, challenges, and future trajectories. Advancements in plasmonic materials, fabrication techniques, and optimization methodologies have significantly enhanced the precision and efficiency of these filters. Metallic materials, dielectrics, and their composites offer diverse options for tailoring spectral responses, while refined fabrication techniques, such as nanolithography and chemical synthesis, allow for the creation of intricate nanostructures. Optimization methods, including genetic algorithms and simulated annealing, facilitate the fine-tuning of filter parameters to achieve desired spectral characteristics. However, challenges persist, including high losses, fabrication complexity, sensitivity to geometry, and integration issues. High losses in plasmonic materials limit signal propagation lengths, affecting the overall efficiency of filters. Fabrication complexity and sensitivity to geometric imperfections hinder large-scale and cost-effective production. Integration challenges, especially with conventional optical components, pose obstacles to seamless system integration. The emerging trends and future prospects in the field are encouraging. Nanophotonic integration and hybrid platforms, active and tunable plasmonic filters, metasurface-based designs, machine learning-assisted optimizations, nonlinear plasmonic effects, and broadband/multiband filters are paving the way for exciting advancements. Integration with machine learning techniques promises faster design iterations and improved performance, while the exploration of nonlinear effects offers innovative functionalities beyond traditional filtering. Plasmonic bandpass filters stand at the frontier of optical communication technology, with a trajectory that emphasizes integration, adaptability, and efficiency. Overcoming current challenges and capitalizing on emerging trends will unlock a future where plasmonic bandpass filters play a central role in enabling high-capacity, dynamic, and reliable optical communication systems.
Data availability
No datasets were generated or analysed during the current study.
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I, Mohammadreza Dashti, wrote and completed the paper. Dr. Emami reviewed it and gave complementary comments. Dr. Zohoori did the final editing.
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Dashti, M., Emami, F. & Zohoori, S. Advancements in the design and optimization of plasmonic bandpass filters for enhanced optical communication systems: a comprehensive review. Discov Electron 1, 10 (2024). https://doi.org/10.1007/s44291-024-00014-1
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DOI: https://doi.org/10.1007/s44291-024-00014-1