Enabling Sustainable Solar Energy Systems Through Electromagnetic Monitoring of Key Components Across Production, Usage, and Recycling: A Review

Abstract

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management.

1. Introduction

The global shift towards sustainable manufacturing is driven by the urgent need to reduce environmental impact, conserve resources, and meet the growing demand for renewable energy [1]. Among renewable energy sources, solar energy stands out as a key contributor to achieving global sustainability goals [2]. Modern solar energy systems primarily employ two conversion technologies: photovoltaic (PV) systems, which directly convert sunlight to electricity via semiconductor junctions (representing approximately 92% of installed capacity), and concentrating solar thermal power (CSP) systems that utilize thermal energy conversion (approximately 8% of capacity) [3]. This review focuses on PV systems due to their dominant market position. The photovoltaic industry is undergoing unprecedented growth, driven by global decarbonization commitments. Over 130 nations have established carbon neutrality targets, with the EU’s REPower EU strategy mandating 600 GW of installed PV capacity by 2030 [4]. Concurrently, China’s 14th Five-Year Plan targets a 20% non-fossil energy share by 2025, predominantly through solar expansion [5]. The International Energy Agency (IEA) projects photovoltaic generation needs to reach 14,000 TWh annually by 2050 to achieve net-zero emissions, representing 35–40% of global electricity supply [6]. This exponential growth of PV deployment creates urgent needs for sustainable manufacturing paradigms.

Solar panels, as the primary equipment for harnessing solar energy, play a critical role in this transition [3]. However, the manufacturing, usage, and end-of-life management of solar energy components must align with sustainable practices to minimize waste, energy consumption, and environmental degradation [7,8,9]. Recent work by Guo et al. [7] and the IEA-PVPS Task 13 [10] underscore the importance of circular economy principles in PV lifecycle management. This necessitates a holistic approach that integrates the Production–Usage–Recycling (PUR) cycle into the lifecycle of solar energy components.

Sustainable manufacturing emphasizes the efficient use of resources, reduction of waste, and minimization of environmental impact throughout a product’s lifecycle [11]. For solar energy components, this means optimizing production processes, ensuring long-term performance during usage, and enabling efficient recycling at the end of their service life. The PUR cycle provides a framework for achieving these goals by addressing each phase of the lifecycle: production (material processing and assembly), usage (performance monitoring and maintenance), and recycling (material recovery and reuse).

A critical enabler of sustainable manufacturing in the PUR cycle is metrology and in-process monitoring. These tools allow for real-time quality control, fault detection [12], and performance optimization, ensuring that solar energy components meet high standards of efficiency and durability. Research by Hwang et al. [13] and Matacena et al. [14,15] has revealed how electromagnetic (EM) sensing methods can improve these processes. Figure 1 demonstrates a PUR cycle with and without metrology and quality control, highlighting the significant improvements in sustainability and efficiency when these tools are integrated.

Photovoltaic monitoring utilizes various approaches, each with distinct operating principles and limitations. Mechanical methods like ultrasonic testing provide localized stress analysis but require surface contact and suffer from signal attenuation in layered materials [16]. Electrical techniques, including IV curve tracing, offer performance data but necessitate system interruption. Thermal imaging detects hotspots yet lacks subsurface resolution [16]. Optical methods such as electroluminescence imaging reveal microcracks but require dark conditions and electrical biasing [17,18]. Electromagnetic (EM) sensing, pioneered by research groups such as Zoughi’s team at Colorado State University [19,20] and Sophian et al. [21], has gained prominence by overcoming these constraints through its unique combination of non-contact operation, volumetric inspection capability, and material-specific sensitivity. EM monitoring addresses three critical challenges in scaling PV production: (1) minimizing silicon waste during manufacturing (currently 35–40% of ingot mass [4]), (2) preventing field failures that account for 0.5–2% annual energy losses [10], and (3) overcoming the less than 20% recycling rates for end-of-life modules [4,22,23]. Among the various frequency monitoring techniques, low- and medium-frequency electromagnetic (EM) sensing methods have emerged as particularly effective due to their non-destructive, real-time, and cost-effective capabilities [24], as demonstrated by Deng et al.’s comprehensive research on EM imaging methods for nondestructive evaluation [24]. Operating in frequency ranges below 10 GHz, these methods can detect defects, monitor material properties, and assess performance without disrupting manufacturing or operational processes. Their ability to provide detailed insights into material and structural integrity makes them indispensable for sustainable manufacturing.

The importance of analyzing the capability of low- and medium-frequency EM sensing methods lies in their potential to enhance sustainability across the PUR cycle. For instance, during production, some techniques enabling precise defect detection and material characterization, reducing waste, and improving product quality are significantly beneficial [13,14,15]. During usage, methods that facilitate continuous performance monitoring, ensuring optimal energy output and early fault detection, can play valuable roles [25,26]. In the recycling phase, EM methods enabling efficient material recovery, minimizing waste, and promoting a circular economy are considerably valuable [27].

This review aims to critically analyze the role of low- and medium-frequency EM sensing methods in the sustainable manufacturing of solar energy components within the PUR cycle. It evaluates which EM techniques are most effective for each phase of the lifecycle, identifies their limitations, and explores opportunities for improving fault detection and process efficiency. By focusing on cost-effective and accessible methods, this study seeks to provide a comprehensive understanding of their capabilities and challenges, ultimately contributing to more sustainable manufacturing practices in the solar energy industry.

The following sections will investigate the principles and applications of low- and medium-frequency EM sensing methods, categorize them by frequency range, and assess their suitability for production, usage, and recycling processes. Through this analysis, the review highlights the transformative potential of these techniques in achieving sustainability across the lifecycle of solar energy components.

2. Electromagnetic Monitoring Methods in Production–Usage–Recycling Cycle of Key Components of Solar Energy

Electromagnetic (EM) monitoring techniques are non-destructive metrology tools that can be beneficial for ensuring the quality, performance, and sustainability of solar energy components throughout their lifecycle [28]. Given the wide range of frequencies for EM sensing, this review categorizes these techniques into low-frequency (<100 MHz) and medium-frequency (100 MHz–10 GHz) methods based on their operational frequency ranges. The list of methods according to their frequency of performance is shown in Figure 2. Each category offers unique capabilities and applications in the production, usage, and recycling phases of solar energy components.

In this section, the focus is on the methods applied in each step of the PUR cycle, though many of these techniques can be used across multiple phases due to their versatility. For example, eddy current testing (ECT), discussed below, can be applied in all three phases of the PUR cycle. Therefore, the LF and MF methods for the three steps of the PUR cycle are discussed below.

2.1. Low-Frequency Electromagnetic (LF-EM) Methods (<100 MHz)

Low-frequency electromagnetic (LF-EM) methods, referred to as LF-EM here, are primarily used for defect detection, material characterization, and quality control in solar energy components. These methods are particularly effective in identifying structural and electrical defects in photovoltaic (PV) materials and modules. The application of LF-EM methods in photovoltaic inspection is grounded in three core principles derived from Maxwell’s equations. First is electromagnetic induction (induced E-field), as described by Faraday’s Law in Equation (1). This relationship yields eddy currents, where Jₑ signifies the current density (A/m²) and σ corresponds to the material conductivity (S/m) in PV components [29].

$$\nabla \times E=-{\displaystyle \frac{\mathsf{\partial }B}{\mathsf{\partial }t}} ; Jₑ=\sigma E ; \sigma ={\omega ϵ″ϵ}_0$$

(1)

where E is electric field intensity (V/m), and B represents magnetic flux density (T).

Secondly, the skin depth effect, as per Equation (2), determines the probing depth. Lower frequencies (<1 MHz) allow for deeper penetration into PV materials. For instance, achieving a depth of approximately 0.5–5 mm in silicon wafers at 10–100 kHz [13].

$$\delta =\sqrt{{\displaystyle \frac{2}{\omega \mu \sigma }}} ; \mu ={\mu }_0{\mu }_r$$

(2)

where δ equals the standard penetration depth (m), ω is defined as the angular frequency (rad/s) = 2πf, μ denotes the magnetic permeability (H/m), and ${\mu }_0$ and ${\mu }_r$ are the magnetic permeability of air and the material, respectively.

Third, the impedance/resonance response characteristics vary between techniques: conventional eddy current testing (ECT) and pulsed eddy current (PEC) methods monitor changes in coil impedance (Z = R + jωL, where Z represents complex impedance (Ω), R is the resistive component (Ω), and L corresponds to inductance (H)), while Radio Frequency Identification (RFID) sensors track shifts in resonant frequency (Δfᵣ), as mentioned in Equation (3) [30].

$$f_r={\displaystyle \frac{1}{2\pi \sqrt{LC}}}; \Delta f_{\mathrm{r}}/f_{\mathrm{r}} \approx -0.5(\Delta L/L)$$

(3)

where fᵣ signifies resonant frequency (Hz), ΔL is inductance change (H), and C is capacitance (F) caused by material degradation [30].

2.1.1. In Production

In production environments, ECT employs time-harmonic excitation (10 kHz–1 MHz) to induce eddy currents [31], with defects causing measurable impedance perturbations, as shown in Equation (4):

$$\Delta Z\propto \int (\mathsf{\partial }Jₑ/\mathsf{\partial }\sigma )·E\ast dV$$

(4)

where ΔZ denotes the impedance change (Ω) and V is the sample volume (m³) that enables the detection of microcracks in silicon cells through phase analysis [32,33], consistent with Wang et al.’s demonstration of eddy current imaging techniques for crack detection in solar cells [33]. This technique also provides precise mapping of TCO sheet resistance uniformity, critical for quality control in thin-film PV manufacturing [13]. Figure 3 demonstrates some examples of ECT applied for fault detection and corrosion on the PV panels.

In contrast to ECT, the pulsed eddy current (PEC) method utilizes transient electromagnetic pulses (1–10 μs duration) to generate decaying eddy currents [37]. The characteristic decay time constant in seconds (τ = μ₀μᵣσd²/π², where d is material thickness in meters) serves as a thickness-dependent metric for quantifying aluminum backsheet thinning due to corrosion and identifying doping inhomogeneities in crystalline silicon [35,38].

Another method, impedance spectroscopy (IS), is a more electrical technique widely used to measure the impedance of a material over a range of frequencies. It is commonly used to analyze charge transport, recombination, and interfacial properties in organic solar cells (OSCs) and perovskite solar cells (PSCs). IS provides critical parameters such as carrier mobility, built-in potential, and doping density, which aid in material optimization and efficiency improvements [39,40]. Impedance spectroscopy (IS) for solar cells involves applying a small sinusoidal voltage, which can be represented as $V\left(t\right)=V_{DC}+V_0 sin\left(\omega t\right)$, and measuring the resulting current response, $I\left(t\right)=I_{DC}+I_0 sin(\omega t+\phi )$, where $\omega$ is the angular frequency, and $\phi$ is the phase angle. The impedance, Z(ω), is defined as the ratio of the voltage to the current, considering both magnitude and phase: $Z={\displaystyle \frac{V_0}{I_0}} exp(-i\phi )=Z_0 cos(\phi )-i Z_0 sin(\phi )$. The impedance can be expressed as Z = Z′ + i Z″, where Z′ is the resistance (real part) and Z″ is the reactance (imaginary part). When the system behaves purely capacitively, it provides insights into dynamic processes within the solar cell [41].

2.1.2. In Usage

For operational PV systems, eddy current imaging (ECI) extends the eddy current principles through multi-frequency impedance mapping (Z(f) = ΣAᵢe^(−2zᵢ/δ(f)), where Aᵢ are amplitude coefficients and zᵢ denote layer depths (m)), enabling layer-specific defect characterization. This approach has proven particularly effective for monitoring crack propagation under thermal cycling conditions and detecting back-contact delamination in field-aged thin-film modules [42,43].

Another LF-EM method increasingly being adopted for real-time diagnostics of PV modules is Radio Frequency Identification (RFID) sensors, due to their ability to measure critical parameters such as voltage, current, and temperature. These sensors enable early fault detection, thereby reducing maintenance costs and improving system reliability [44,45]. Concurrently, passive RFID sensors (125 kHz–13.56 MHz) have emerged as a robust solution for real-time condition monitoring, where corrosion-induced inductance changes and temperature-dependent resistance variations in module frames and interconnects are tracked through resonant frequency shifts (Δfᵣ) obtained from Equation (3) and backscatter modulation [46,47].

Table 1. Summary of key performance metrics of LF-EM.

Method	Frequency Range	Key Metrics	PV Defects Detected	Application
ECT	10 kHz–1 MHz	ΔZ, phase angle	Microcracks (≥0.2 μm), TCO sheet resistance non-uniformity	Inline quality control of wafers & coatings
PEC	DC–100 kHz	τ, peak amplitude	Backsheet thinning (10–500 μm), doping variations (5–15% non-uniformity)	TCO & encapsulation defect analysis
RFID	125 kHz–13.56 MHz	Δfr, Q-factor	Frame corrosion (0.1–2 mm/year), temperature hotspots (ΔT > 5 °C)	Real-time PV diagnostics

provides a summary of key performance metrics of the described LF methods for PV modules or solar panels.

These techniques collectively provide a comprehensive PV inspection toolkit, where ECT/PEC deliver superior resolution (<100 μm) for manufacturing QA, while RFID offers wireless monitoring capabilities. The fundamental frequency–depth–material property relationships allow for optimized inspection protocols throughout the PV system lifecycle, particularly in the production and usage phases.

2.1.3. In Recycling

Certain EM technologies are particularly effective in the recycling of PV panels, which are primarily made of non-ferrous materials. One example is electrodynamic eddy current separation (ECS), a method used to extract non-ferrous metals such as aluminum, copper, and brass from retired PV panels. This technique induces eddy currents in conductive materials, creating a repulsive force that aids in the efficient separation of metals from mixed waste streams [48].

The operational benefits of ECS directly result in economic advantages for PV recycling. As a non-contact method based on fundamental conductivity differences, ECS avoids the costs of consumable and preprocessing requirements associated with traditional separation technologies. Industrial applications demonstrate three main economic benefits [49,50]:

• Elimination of consumables and chemical inputs;
• Continuous processing capability without the need for pretreatment;
• Reduced maintenance requirements due to non-contact operation.

Therefore, ECS is a cost-effective option for recovering non-ferrous metals from PV waste streams.

Another LF-EM method commonly used for material tracking is RFID. RFID tags embedded in PV panels during manufacturing enable the efficient sorting and processing of materials at the recycling stage. As demonstrated by Kantareddy et al. and Węglarski et al., RFID tags embedded in PV panels [44,45,51] during manufacturing enable efficient sorting and processing of materials at the recycling stage. These tags store information about the panel’s composition, manufacturing date, and material constituents, ensuring accurate identification and recovery of valuable elements like silicon and silver [44,45,51].

2.2. Medium-Frequency Electromagnetic (MF-EM) Methods (100 MHz–10 GHz)

Medium-frequency (MF) electromagnetic techniques, referred to as MF-EM here, provide essential capabilities for characterizing photovoltaic systems throughout the production, operation, and recycling phases. These methods utilize fundamental wave–material interactions governed by Maxwell’s equations, offering superior resolution (on the scale of micrometers) and material specificity compared to low-frequency approaches [52]. Based on the research conducted, three core physical phenomena underpin their operation in PV systems.

Firstly, the dielectric response of PV materials is characterized by the complex permittivity ϵ* = ϵ′ − jϵ″, where ϵ′ represents the real component describing polarization effects (unitless) and ϵ″ denotes the imaginary component accounting for dielectric losses. This relationship determines the power dissipation density obtained from Equation (5).

$$P={2\pi fϵ}_0ϵ″{\left|E\right|}^2$$

(5)

where P is the absorbed power density (W/m³), f is the operating frequency (Hz), ϵ₀ is the permittivity of free space (8.854 × 10⁻¹² F/m), and E is the electric field intensity (V/m) [53]. Secondly, the penetration depth δ_p, which governs inspection resolution, is as follows:

$${\delta }_p=\left({\displaystyle \frac{c}{2\pi f\sqrt{{ϵ}^{\prime }}}}\right)\sqrt{\left(0.5\right(\surd (1+{tan}^2\delta )-1)} ; (tan\delta =ϵ″/ϵ\prime )$$

(6)

where c is the speed of light (3 × 10⁸ m/s) and tanδ = ϵ″/ϵ′ represents the loss tangent. Equation (6) illustrates the frequency-dependent tradeoff between resolution and penetration depth, with typical δ_p values ranging from 0.1 mm at 10 GHz to 5 mm at 100 MHz in silicon-based PV materials [54].

The third phenomenon, wave reflection at material interfaces, is quantified by the reflection coefficient as shown in Equation (7):

$$\Gamma ={\displaystyle \frac{Z_{material}-Z_0}{Z_{material}+Z_0}}; Z_{material}=\sqrt{({\mu }_0/{ϵ}^{\ast })}$$

(7)

where $Z_{material}$ represents the material impedance in ohms, and Z₀ = 377 Ω is the free-space impedance. This relationship allows for the non-contact characterization of subsurface features through reflectometry measurements [55].

2.2.1. In Production

Microwave imaging techniques are widely used for the non-destructive evaluation of silicon wafers and thin-film solar cells [19,38,56,57,58,59]. The principles and components for Microwave NDT testing are demonstrated in Figure 4. These methods offer reliable detection of defects and structural irregularities, making them suitable for quality control during production.

In production environments, Microwave NDT utilizes continuous-wave (1–10 GHz) or pulsed excitation to measure transmission coefficients (S₂₁) in Equation (8), where α is the attenuation constant and d represents material thickness:

$$S_{21}\propto e^{-\alpha d}; \alpha =2\pi f\surd ϵ″/c$$

(8)

This approach enables the detection of 10–100 µm voids in silicon cells through permittivity contrast (Δϵ′ ≈ 2–3) and TCO conductivity variations (σ in Equation (1)) with an accuracy of ±5% [57,58].

2.2.2. In Usage

One example of MF non-contact process monitoring utilizes microwave reflectometry (MR), which has emerged as a powerful technique for the in situ monitoring of moisture ingress and delamination in operational PV modules. The method measures changes in the complex reflection coefficient, the frequency-dependent form of Equation (7), Γ(f) = [Z_PV(f) − Z₀]/[Z_PV(f) + Z₀], where Z_PV(f) represents the frequency-dependent impedance of the PV module and Z₀ = 50 Ω is the reference impedance [20,60]. Practical implementations utilize compact patch antennas integrated within junction boxes, achieving 0.5 mm spatial resolution through the precise measurement of both magnitude (Δ|Γ| > 0.1 for 1 mm² corrosion spots) and phase shifts (>5° for crack detection) [61,62,63]. This aligns with findings by Zolfaghari et al. [35], who demonstrated similar sensitivity in defect detection using Microwave Phased Testing (MPT), where a 0.5 mm resolution reliably identified surface cracks in welded materials, with full detection certainty for defects exceeding 2.5 mm [64]. Field trials have demonstrated 92% accuracy in identifying backsheet degradation using 2.45 GHz ISM band operation, with the technique being particularly sensitive to moisture ingress due to the large permittivity contrast between water (ϵ′ ≈ 80) and ethylene–vinyl acetate (EVA) encapsulant (ϵ′ ≈ 2.9) [65].

Another method, dielectric spectroscopy (DS), provides complementary capabilities for monitoring encapsulant aging and potential-induced degradation through the broadband (100 MHz–3 GHz) measurement of complex permittivity variations. The technique builds on foundational work by Agroui et al. [66] on EVA encapsulant characterization. This method employs interdigital capacitors printed directly on module edges to track ϵ′ and ϵ″ evolution, where Δϵ′ > 0.5 indicates Ethylene-Vinyl Acetate (EVA), the primary encapsulant material in PV modules, and crosslinking loss, and ϵ″ increases at 1–2 GHz reveal Na⁺ migration associated with PID effects [66]. Implementation in a 1 MW power plant demonstrated 89% fault prediction accuracy by monitoring key parameters, including the degradation rate (dϵ′/dt > 0.05/year signaling failure) and moisture content (Δϵ″ > 0.3 at 1 GHz corresponding to 0.1% water content). The method’s 10 mW power requirement and 100 μm resolution make it particularly suitable for the long-term monitoring of utility-scale installations [67].

The other widely used method, Passive Microwave Radiometry (PMR), offers a completely non-invasive monitoring solution through the detection of thermal emissions in the 1–10 GHz range. The brightness temperature T_B = ηT_phys + (1 − η)T_amb, where η is emissivity (0.9 for Si, 0.3 for glass) and T_phys is the physical temperature, enables hotspot detection with 0.5 °C resolution at 4 GHz [68]. Field deployments [69,70,71,72] in desert environments achieved over 90% identifying hotspots, while also monitoring microcracks by observing changes in emissivity (Δη > 0.05). The passive nature of this technique (0 W transmission power) eliminates interference concerns while providing 10 cm spatial resolution in array configurations [69,70,71,72].

Implementation strategies vary according to system scale and criticality. Utility-scale installations benefit from combined PMR and DS systems, providing comprehensive thermal and material degradation monitoring. Rooftop applications typically employ MR for its cost-effectiveness and simple integration, while critical installations may utilize hybrid DS-MR systems for enhanced reliability [IEC 62941:2016] [73,74]. All methods operate exclusively in civilian frequency bands (ISM 2.45/5.8 GHz) and comply with international standards for PV monitoring (IEC 62793), ensuring both technical efficacy and regulatory compliance [FCC Part 15/ETSI EN 300 440] [75].

These microwave techniques have demonstrated significant advantages over conventional monitoring approaches, including reduction in inspection costs through automated unmanned aerial vehicle (UAV)-based MR systems and improvement in early fault detection rates compared to visual inspection methods [76]. The physical principles underlying these methods, particularly the frequency-dependent interactions with PV materials described by Equations (5)–(8), enable the targeted monitoring of specific degradation mechanisms while maintaining the non-destructive, non-invasive characteristics essential for operational PV systems.

2.2.3. In Recycling

Microwave-assisted delamination and pyrolysis have emerged as transformative technologies for recycling photovoltaic modules, taking advantage of the frequency-dependent dielectric properties of PV materials. The delamination process exploits the significant difference in dielectric loss between silicon nitride anti-reflection coatings (ϵ″ ≈ 0.1 at 2.45 GHz) and EVA encapsulant (ϵ″ ≈ 0.01). The absorbed power density follows Equation (5), enabling the selective heating of interfacial layers [38], allowing for industrial-scale 25 kW systems operating at 2.45 GHz to achieve complete layer separation in under 2 min through controlled heating to 300 °C. Thermal stresses (Δσ = EΔαΔT) are generated by the coefficient of thermal expansion mismatch (Δα ≈ 5 × 10⁻⁶ K⁻¹) between glass and polymer components. This process preserves over 98% of silicon cells for reuse while reducing energy consumption by 40% compared to conventional thermal delamination [77,78,79].

Microwave-assisted pyrolysis extends this principle to material recovery through localized heating governed by T_local ≈ Pλ²/8π²κr², where κ is the thermal conductivity of the PV waste matrix (0.2–0.5 W/m·K for typical module debris) [78]. Operating at 915 MHz with 50 kW power [80], these systems efficiently decompose polymer backsheets, with EVA films exhibiting near-complete weight loss (99.7%) at a peak exothermic temperature of 438 °C—significantly lower than conventional thermal methods [79]. This process maintains silicon wafer integrity while achieving more than 90% metal recovery rates (Jung et al., 2016) [81] and 95% glass purity (Komoto et al., 2018) [82], as demonstrated in recent industrial-scale trials [50,79,83]. The frequency selection is critical, with 915 MHz providing deeper penetration (δ_p ≈ 15 cm) into bulk material compared to 2.45 GHz systems while maintaining sufficient energy coupling to organic components.

By leveraging these cost-effective and accessible techniques, as summarized in

Table 2. Functionality of some MF of EM methods.

Method	Frequency Range	Key Metrics	PV Applications
Microwave NDT	1–10 GHz	Γ, S21	Void/crack detection in PV modules
Non-Contact Microwave Sensors	5.8–24 GHz	fr, Δf	Structural health monitoring (PV arrays)
Microwave Heating	2.45 GHz	P, Tlocal	Delamination/recycling of PV materials
Microwave Pyrolysis	0.5–3 GHz	P, Tlocal	Polymer decomposition and material recovery (e.g., EVA, backsheets)

, manufacturers can improve production processes, extend the lifespan of PV modules, and promote sustainable recycling practices, contributing to the global transition toward renewable energy.

Below,

Table 3. Summary of suitability of LF- and MF-EM measurement methods for PUR cycle of PV modules.

Frequency of Performance	Methods	Show Capability in Cycle of the Following:			Reasoning
		Production	Usage	Recycling
LF-EM	Eddy current testing (ECT) [13,48]	Yes	Yes	Yes	ECT operates at low frequencies, ideal for inspecting conductive layers, monitoring corrosion, and separating metals.
LF-EM	Pulsed eddy current (PEC) [37,84]	Yes	Yes	Yes	PEC uses transient electromagnetic fields for defect detection, corrosion monitoring, and metal recovery.
LF-EM	Electrodynamic eddy current separation (ECS) [48]	No	No	Yes	ECS is specialized for separating conductive materials during recycling, not for production or usage.
LF-EM	RFID sensors [45,51]	Yes	Yes	Yes	RFID operates at low frequencies and is used for tracking materials and components across all lifecycle steps.
LF-EM	Impedance spectroscopy (IS) [39,40]	Yes	Yes	No	IS operates in medium frequencies, suitable for characterizing electrical properties and monitoring degradation, but not used in recycling.
LF-EM	Eddy current imaging [13]	Yes	Yes	No	Provides high-resolution imaging of defects in conductive layers, but not designed for recycling.
MF-EM	Microwave NDT [38,56]	Yes	Yes	No	Microwave NDT operates at high frequencies, detecting defects in dielectric materials, but not used for recycling.
MF-EM	Microwave Photonic Sensing (MPS) [85]	Yes	Yes	No	MPS uses high-frequency microwaves for precise sensing, but not designed for recycling.
MF-EM	Non-contact process monitoring [85]	Yes	Yes	No	Enables real-time monitoring using high-frequency signals, but is not used in recycling.
MF-EM	Microwave-assisted pyrolysis [45,51]	No	No	Yes	Used for breaking down polymers during recycling, not for production or usage.
MF-EM	Microwave heating for delamination [79]	No	No	Yes	Used for separating layers in recycling, not for production or usage.

provides a summary of the categorization and analysis of LF- and MF-EM methods and their capability for use in each phase of the PUR cycle. In addition, the table summarizes the key advantages of EM monitoring techniques across the Production–Usage–Recycling cycle, highlighting their role in reducing material waste, enhancing energy efficiency, and ensuring long-term reliability.

Microwave-assisted delamination offers inherent economic advantages due to its fundamental operating principles. The frequency-selective heating mechanism (Equation (5) allows for targeted energy deposition, especially at material interfaces, reducing thermal losses to non-target components. This selectivity results in energy savings, as only the dielectric loss components (such as EVA encapsulants and backsheets) absorb significant microwave energy, while silicon cells remain mostly transparent to the applied fields [86]. Industrial implementations have shown that the rapid, volumetric heating characteristic of microwave systems (completing delamination in less than 2 min) decreases processing time and ancillary energy requirements compared to traditional thermal methods. The preservation of intact silicon wafers (with over 98% recovery) and high-purity glass components further enhances economic viability by maintaining material value, a crucial aspect in recycling economics [50]. Frequency optimization (915 MHz vs. 2.45 GHz) provides additional cost benefits by improving penetration depth (δ_p ≈ 15 cm), allowing for the processing of intact modules without the need for energy-intensive size reduction [78,86]. These combined factors position microwave-assisted recycling as a technologically and economically compelling solution for end-of-life PV management.

3. Challenges, Future Trends, and Opportunities

3.1. Advantages of EM Techniques for Sustainable Manufacturing

Electromagnetic (EM) monitoring techniques have emerged as indispensable tools in the sustainable manufacturing of solar energy components, offering significant advantages in material efficiency, energy optimization, and product reliability. These techniques support the reduction of material waste by enabling early defect detection during production. For instance, RFID sensors enhance sustainability by continuously monitoring PV module performance, detecting faults such as shading and degradation early, which aids in proactive maintenance and reduces replacement waste [45]. Similarly, ECT provides non-invasive solder joint and interconnection quality assessments, detecting weak electrical connections before they cause performance degradation [13,84]. By integrating these EM techniques into manufacturing processes, industries can optimize resource utilization, minimize defective output, and contribute to sustainable production practices through early defect detection and waste reduction.

Figure 5 shows the different faults that can be detected by EM sensors in various parts of a solar cell.

EM techniques also enhance energy efficiency by optimizing manufacturing processes. For example, impedance spectroscopy (IS) enables the precise electrical characterization of solar cells, optimizing charge transport and minimizing recombination losses, and directly contributing to higher energy conversion efficiency [39,40]. Microwave NDT provides reliable detection of defects in dielectric materials, ensuring uniform film deposition and enhancing solar cell performance [87]. Additionally, Microwave Photonic Sensing (MPS) analyzes temperature variations across PV panels, enabling real-time performance optimization and reducing energy losses [88]. By integrating these cutting-edge techniques, PV manufacturing processes become more energy-efficient, reducing waste and enhancing sustainability in solar energy production.

Ensuring the long-term durability and reliability of solar energy components is another critical advantage of EM techniques. Eddy current imaging enables real-time defect detection in utility-scale photovoltaic (PV) plants, ensuring proactive maintenance and preventing performance degradation [13,40]. RFID-based monitoring automates fault diagnosis in PV modules, enhancing predictive maintenance and reducing long-term energy losses [45,51]. Multi-frequency ECT assesses solder joint and interconnection quality, preventing premature failures caused by mechanical stress or environmental degradation [13,84]. These techniques collectively enhance the solar module lifespan, reduce maintenance costs, and ensure reliable long-term performance in solar energy systems.

3.2. Challenges and Limitations

Despite their advantages, EM monitoring techniques face several challenges and limitations. One major limitation is the sensitivity and accuracy of EM sensors, which can be affected by environmental factors such as temperature fluctuations, electromagnetic interference, and material heterogeneity. For example, Du et al. determined that Eddy Current Thermography, while effective for detecting microcracks, requires precise calibration to differentiate between deep and surface-level defects [89]. Additionally, RFID sensors may struggle with low-mobility materials and background irradiance, necessitating advanced signal processing techniques to enhance reliability [45].

Another challenge is the integration of EM systems into high-speed production lines. Techniques such as Microwave NDT are hindered by slow scanning speeds, making real-time inspection challenging [38,51]. Overcoming these integration challenges is crucial for ensuring high-speed, cost-effective, and reliable defect detection in PV manufacturing processes.

While electromagnetic methods demonstrate clear technical advantages, their economic viability depends on scale. Large-scale implementations (>10,000 tonnes/year) achieve cost parity with conventional methods through equipment utilization rates, but smaller operations face capital cost barriers [50,79]. Recent innovations in modular EM systems show promise for reducing upfront investments by 40–50%, as demonstrated by Amir et al. [90]. The potential for cost-effective solutions is highlighted through their work on low-cost waste sorting techniques using eddy current separation processes [90]. In addition, suitable sensors, data acquisition hardware, and software can be prohibitively expensive, limiting widespread adoption [24]. Additionally, calibration and standardization issues complicate the implementation of EM monitoring across diverse manufacturing processes. Techniques such as impedance spectroscopy (IS) require precise parameter extraction and careful device modeling, demanding accurate calibration for reliable results [39,40].

Table 4. Benefits, limitations, challenges, and proposed solutions for EM monitoring in solar panel manufacturing.

Aspect	Details
Benefits for Sustainable Manufacturing
Production	Reduces material waste, optimizes energy use, and improves process efficiency [24,38,51]
Usage	Ensures long-term reliability and performance of solar panels [45,51]
Recycling	Enables efficient material recovery and reduces environmental impact [24,27]
Limitations
Sensitivity	EM sensors may struggle with detecting micro-defects in heterogeneous materials [89]
High Initial Cost	High initial investment for advanced EM monitoring systems, including sensors, hardware, and software, are expensive, limiting SME adoption [24]
Integration	Challenges in adapting EM systems to high-speed production lines [38,51]
Calibration Issues	Techniques like IS require precise parameter extraction and advanced modeling [39,40]
Challenges in Solar Panels
Defect Detection	Difficulty in identifying microcracks and delamination in thin-film PV materials [13,84]
Durability	Monitoring long-term degradation under harsh environmental conditions [45,51]
Recycling	Lack of standardized methods for EM-based sorting of end-of-life PV materials [13,27]
Proposed Solutions
AI Integration	Use machine learning to improve defect detection accuracy and adaptability. CNNs improve defect detection accuracy (e.g., >85% for thermographic analysis) [89,91]
Miniaturized Sensors	Develop portable EM sensors for flexible and cost-effective deployment [24]
Standardization	Establish industry-wide protocols for EM monitoring in PV manufacturing and recycling [39,40]
Academia–Industry Collaboration	Joint research accelerates cost-effective, high-performance EM system development [38,51]

provides a comprehensive overview of the benefits, limitations, challenges, and proposed solutions for EM monitoring in solar panel manufacturing, offering actionable insights for overcoming current barriers.

3.3. Future Trends and Opportunities

To address these challenges, several future trends and opportunities are emerging. The integration of artificial intelligence (AI) and machine learning (ML) into EM signal interpretation represents a promising avenue for enhancing the accuracy and reliability of defect detection. For example, Due et al. demonstrated that ECT combined with convolutional neural networks (CNNs) has improved the speed and accuracy of identifying surface and subsurface defects in silicon PV cells [89]. Similarly, AI-driven models automate the analysis of RFID-based monitoring data, identifying faults quickly and non-invasively [45,51]. Recent advances extend beyond CNNs to incorporate backpropagation (BP) neural networks for time-series impedance spectroscopy analysis, achieving 96.3% accuracy in predicting cell degradation pathways by learning complex nonlinear relationships in the data, as shown by Chang et al. and Lu et al. [92,93].

Such AI integrations significantly enhance real-time PV quality control, reduce inspection costs, and improve the reliability and sustainability of solar energy systems. Emerging physics-informed machine learning approaches are particularly transformative, embedding Maxwell’s equations directly into neural network architectures to maintain physical consistency while learning from empirical data [85]. For instance, graph neural networks (GNNs) applied to distributed EM sensor arrays can model spatial dependencies across utility-scale PV plants, enabling system-level fault diagnosis with higher precision [86]. These intelligent systems are being progressively deployed in edge computing devices, allowing real-time analysis without cloud dependency—a critical advancement for field applications [94,95].

Other future trends include the development of miniaturized and portable EM sensors, which can be deployed in remote or resource-constrained settings, democratizing access to EM monitoring technologies [96]. The expansion of EM techniques to emerging PV technologies, such as perovskite and organic solar cells [97], also presents significant opportunities. These next-generation materials often exhibit unique electromagnetic properties that require specialized monitoring approaches, creating a demand for innovative EM solutions. Finally, collaboration between academia and industry is essential for driving sustainable innovations in EM monitoring. Joint research initiatives can accelerate the development of cost-effective, high-performance EM systems, while industry partnerships can facilitate the commercialization and scaling of these technologies.

4. Conclusions

This review has demonstrated the transformative potential of electromagnetic (EM) monitoring techniques in advancing sustainable manufacturing and materials processing across the Production–Usage–Recycling (PUR) cycle of solar energy components. By integrating EM techniques into each phase of this cycle, the solar energy industry can achieve significant improvements in material efficiency, process optimization, and product reliability, while minimizing environmental impact and promoting a circular economy.

The EM monitoring framework presented here directly contributes to United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) by improving PV system reliability and efficiency, and Goal 12 (Responsible Consumption and Production) through enhanced material recovery rates [98]. These alignments position EM technologies as key enablers of sustainable energy transitions.

The integration of EM monitoring techniques into the PUR cycle represents a significant step toward achieving sustainable manufacturing goals in the solar energy industry. By reducing material waste, optimizing energy efficiency, and enhancing the durability and recyclability of solar energy components, EM techniques contribute to a more sustainable and circular economy. This review underscores the importance of continued research and innovation in EM monitoring to unlock its full potential and drive the transition to a greener future. The methodological framework outlined in this document connects essential electromagnetics with the requirements of industrial sustainability, offering (1) a physics-based method for quality control that reduces silicon waste by enabling early defect identification, (2) non-invasive monitoring solutions that extend module lifetimes, and (3) material recovery processes that achieve greater purity for closed-loop manufacturing. Collectively, these advancements address the solar industry’s trilemma of scaling production while reducing environmental impact and maintaining cost competitiveness.