This paper presents three self-powered photodetectors namely, p ⁺ -bilayer graphene (BLG)/n ⁺ -ZnO nanowires (NWs), p ⁺ -BLG/n ⁺ -Si NWs/p ^– -Si and p ⁺ -BLG/n ⁺ -ZnO NWs/p ^– -Si. The Silvaco Atlas TCAD software is utilized to characterize the optoelectronic properties of all the devices and is validated by analytical modeling. The proposed dual-junction photodetectors cover broadband spectral response varying from ultraviolet to near-infrared wavelengths. The dual-heterojunction broadband photodetector exhibits photocurrent switching with the rise and fall time of 1.48 and 1.27 ns, respectively. At −0.5 V bias, the highest external quantum efficiency, photocurrent responsivity, specific detectivity, and the lowest noise equivalent power of 71%, 0.28 A W ⁻¹ , 4.2 × 10 ¹² cmHz ^1/2 W ⁻¹ , and 2.59 × 10 ^–17 W, respectively, are found for the dual-heterojunction device with a wavelength of 480 nm at 300 K. The proposed nanowires based photodetectors offer great potential to be utilized as next-generation optoelectronic devices.

In this study, the authors present three different metamaterial absorbers (MMAs) namely, T, split‐I (SI) and split‐Jerusalem cross (SJC) with different dimensions and geometrical configurations. The absorption rates of T‐, SI‐ and SJC‐shaped absorbers are studied at microwave frequencies. T‐shaped absorber demonstrates perfect absorption at 10.19 GHz, making it suitable for single band operation, whereas SI‐shaped absorber exhibits two perfect absorption peaks at 9.32 and 10.75 GHz finding its application in dual band operation. However, novel SJC‐shaped absorber demonstrates multiple absorption peaks at 9.92, 10.42, 10.93, 11.75 and 13.25 GHz with absorption of 99.5, 91, 99.9, 91.8 and 99.6%, respectively, making it suitable for X‐ and Ku‐band operations. The proposed MMAs have a thickness of around 0.8 mm (i.e. < λ /37) with respect to the lowest frequency of operation. Furthermore, the absorbers are analysed for different angles of polarisation and incidence for transverse electric polarised wave with a step size of 15°. The proposed absorbers have been fabricated and experimentally demonstrated at X‐band verifying the results obtained from simulations and implementing an equivalent circuit method. Further, SJC‐shaped absorber is demonstrated for multi‐and wide‐band terahertz applications exhibiting four perfect absorption peaks at 2.76, 2.89, 3.02 and 3.31 THz.

We present novel p ⁺ -bilayer graphene (BLG) and mercury cadmium telluride (MCT)-based single- and dual-junction photodetectors, namely, p ⁺ -BLG/n ^- -MCT and p ⁺ -BLG/n ^- -MCT/n ⁺ -MCT, operating in long infrared regime. The optoelectronic characterizations utilizing Silvaco Atlas TCAD are validated by analytical modeling. All the devices demonstrate self-powered mode operation and exhibit more than 10 ⁶ times enhancement in photocurrent density. The dual-heterojunction photodetector demonstrates rapid photocurrent switching with the rise and fall time of ~0.05 and ~0.013 ps, respectively, than that of single-heterojunction-based photodetectors. The highest external quantum efficiency (QE _ext ), external photocurrent responsivity, and lowest noise equivalent power of 85.8%, 7.33 A/W, and 4.72 × 10 ^-20 W, respectively, are found for the dual-heterojunction photodetector with a wavelength of 10.6 μm at 77 K. Such optimum photodetection performance is attributed to the presence of a huge amount of electric field (180 kV/cm) at n ^- -n ⁺ heterojunction, which accelerates the photogenerated electrons resulting in effective photocurrent. It is further demonstrated that the temperature-dependent QE _ext with values >100% is due to the carrier multiplication effect in BLG.

Graphene and zinc oxide (ZnO) nanowires (NWs)-based photodetectors demonstrate excellent photodetection performance in the ultraviolet (UV) spectrum regime. This paper presents the design and analysis of a heterostructure model of p⁺-few-layer graphene (p⁺-FLG)/n^–-ZnO NWs-based UV photodetector. The design utilizes the unique properties of few-layer graphene to enhance light absorption and improve photodetector performance. The analysis under both self-biasing and conductive modes of operation reveals that the integrated electric field and the photovoltaic effect at the p⁺-FLG/n⁻-ZnO NWs hetero-interface create a rectifying behavior. The photodetector achieves an external photocurrent responsivity, external quantum efficiency, detectivity, and noise equivalent power of 0.12 A/W, 44.1%, 1.9 × 10⁹ Jones, and 5.6 × 10^–14 W, respectively, under UV illumination at 350 nm, 0 V bias, and 300 K. Additionally, the photodetector exhibits ultrafast photoswitching rise and fall times of 0.26 ns and a 3-dB cut-off frequency of 1.31 GHz. The comparative analysis with existing photodetectors demonstrates that the proposed model surpasses many in sensitivity, speed, and efficiency. The enhancement of charge collection with the applied reverse-biased voltage results in a response time of 0.16 ns, a peak photocurrent responsivity of 0.2 A/W, a maximum external quantum efficiency of 61%, a peak detectivity of 2.4 × 10⁹ Jones, and minimum noise equivalent power of 4.4 × 10^–14 W at − 0.5 V. The findings inspire the development of next-generation self-driving, highly efficient, broadband photodetectors, and other economically viable and multifunctional optoelectronic devices.

Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively. Yet, this design typically falls short in leveraging shallow structural information to enrich the dual branches with comprehensive multiscale data. Additionally, the lightweight components struggle to capture the global contextual details of feature sets efficiently. When compared to state-of-the-art models, lightweight semantic segmentation models usually exhibit performance gaps. To address these issues, we introduce a novel approach that incorporates a deep-shallow interaction mechanism with an attention module to improve water body segmentation efficiency. This method spatially adjusts feature representations to better identify water-related data, utilizing a U-Net frame work to enhance the accuracy of edge detection in water zones by providing more precise local positioning information. The attention mechanism processes and merges low and high-level data separately in different dimensions, allowing for the effective distinction of water areas from their surroundings by blending spatial attributes with in-depth context insights. Experimental outcomes demonstrate a remarkable 95% accuracy, showcasing the proposed method’s superiority over existing models.

This research explores the design of an infrared (IR) photodetector using mercury cadmium telluride (Hg_1–xCd_xTe). It proposes two- and three-dimensional homojunction models based on p⁺-Hg_0.7783Cd_0.2217Te/n^–-Hg_0.7783Cd_0.2217Te, focusing on applications in the long-wavelength infrared range. The photodetector’s performance is analyzed using Silvaco ATLAS TCAD software and compared with analytical calculations based on drift-diffusion, tunneling, and Chu’s approximation techniques. Optimized for operation at 10.6 μm wavelength under liquid nitrogen temperature, the proposed photodetector demonstrates promising optoelectronic characteristics including the dark current density of 0.20 mA/cm², photocurrent density of 4.98 A/cm², and photocurrent density-to-dark current density ratio of 2.46 × 10⁴, a 3-dB cut-off frequency of 104 GHz, a rise time of 0.8 ps, quantum efficiency of 58.30 %, peak photocurrent responsivity of 4.98 A/W, specific detectivity of 3.96 × 10¹¹ cmHz^1/2/W, and noise equivalent power of 2.52 × 10^–16 W/Hz^1/2 indicating its potential for low-noise, high-frequency and fast-switching applications. The study also incorporates machine learning regression models to validate simulation results and provide a predictive framework for performance optimization, evaluating these models using various statistical metrics. This comprehensive approach demonstrates the synergy between advanced materials science and computational techniques in developing next-generation optoelectronic devices. By combining theoretical modeling, simulation, and machine learning, the research highlights the potential to accelerate progress in IR detection technology and enhance device performance and efficiency. This multidisciplinary methodology could serve as a model for future studies in optoelectronics, illustrating how advanced materials and computational methods can be utilized to enhance device capabilities.