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Published
, Published online: 25 July 2025
, doi: 10.37188/lam.2025.039
Perovskite photovoltaics upholds the most prominent position in the field of tandem technology development. In this aspect, the creation of perovskite material with suitable bandgap (≥ 1.65 eV) is necessary. And in order to achieve the best device characteristics, the high-quality film formation is crucial. To get a high-quality film, the solvent engineering approach stays at the forefront. However, although the solvent engineering was well discussed for such conventional material as MAPbI3, the field of wide bandgap perovskite materials is still lacking in this area. This paper presents the solvent engineering approach to improve the efficiency and stability of the conventional wide bandgap perovskite material Cs0.17FA0.83PbI1.8Br1.2. Here we utilize several solvents such as traditional N,N-dimethylformamide, dimethyl sulfoxide, N-methyl-2-pyrrolidone and acetonitrile. It was demonstrated that implication of any binary DMF-X solvent improves the solar cell efficiency compared to the pure DMF solution, but the ratio of the X solvent is unique for every X and the foundation for the X influence is also unique. The addition of 2.4 M of DMSO is considered the best to improve the stability and efficiency of laboratory devices, however implementation of AcN allowed to produce 25 cm2 mini-modules with the PCE reaching 10%.
Published
, Published online: 10 July 2025
, doi: 10.37188/lam.2025.033
The largest ground-based telescopes will be much larger than their space-based counterparts far into the future. Remote sensing problems that can take advantage of active and adaptive wavefront control that correct the incoming atmospherically distorted optical wavefront can benefit from very large ground-based telescopes that have other important advantages. For example, their much lower cost (typically one or two orders of magnitude less) and shorter time-to-completion can be compelling. For optical or IR problems that require high angular resolution and large photometric dynamic range we suggest that techniques that make use of photonics, machine learning, or additive manufacturing may even enable less expensive specialized telescopes that are larger than what astronomers are currently building. The Instituto de Astrofísica de Canarias (IAC) recently began a 5 year program with support from the European Union called the Laboratory for Innovation in Opto-mechanics. Its goal is to show how technology innovations can enable less costly and larger telescopes, in particular, aimed at the problem of finding extrasolar life within a few parsecs of the Sun.
Published
, Published online: 04 June 2025
, doi: 10.37188/lam.2025.040
This study proposes a novel heterodyne grating interferometer designed to meet the multi-dimensional atomic-level measurement demands of next-generation lithography systems and large-scale atomic-level manufacturing. By utilizing a dual-frequency laser source, the interferometer enables simultaneous three-degree-of-freedom (3-DOF) displacement measurements. Key innovations include a compact, zero dead-zone optical path architecture, which enhances measurement robustness by minimizing sensitivity to laser source instabilities and atmospheric refractive index fluctuations. In addition, we present a systematic crosstalk error analysis, coupled with a corresponding compensation algorithm, effectively reducing crosstalk-induced errors to below 5%. Experimental evaluation of the 90 × 90 × 40 mm3 prototype demonstrates outstanding performance metrics: sub-nanometer resolutions (0.25 nm for X/Y-axes, 0.3 nm for Z-axis), superior linearity coefficients (6.9 × 10−5, 8.1 × 10−5, 16.2 × 10−5 for X-, Y-, and Z-axes, respectively), high repeatability (0.8 nm@1000 nm for all axes), exceptional long-term stability (20 nm XY-plane drift, 60 nm Z-axis drift over 1000 s), and practical measurement ranges exceeding 10 mm in-plane and 2 mm axially. Comparative analysis with state-of-the-art sensors demonstrates significant advantages in measurement precision, system integration, and multi-axis capability. This advancement highlights excellent potential for applications in integrated circuit fabrication, atomic-scale manufacturing, and ultra-precision metrology for aerospace systems.
Published
, Published online: 29 May 2025
, doi: 10.37188/lam.2025.032
Defect inspection is critical in semiconductor manufacturing for product quality improvement at reduced production costs. A whole new manufacturing process is often associated with a new set of defects that can cause serious damage to the manufacturing system. Therefore, classifying existing defects and new defects provides crucial clues to fix the issue in the newly introduced manufacturing process. We present a multi-task hybrid transformer (MT-former) that distinguishes novel defects from the known defects in electron microscope images of semiconductors. MT-former consists of upstream and downstream training stages. In the upstream stage, an encoder of a hybrid transformer is trained by solving both classification and reconstruction tasks for the existing defects. In the downstream stage, the shared encoder is fine-tuned by simultaneously learning the classification as well as a deep support vector domain description (Deep-SVDD) to detect the new defects among the existing ones. With focal loss, we also design a hybrid-transformer using convolutional and an efficient self-attention module. Our model is evaluated on real-world data from SK Hynix and on publicly available data from magnetic tile defects and HAM10000. For SK Hynix data, MT-former achieved higher AUC as compared with a Deep-SVDD model, by 8.19% for anomaly detection and by 9.59% for classifying the existing classes. Furthermore, the best AUC (magnetic tile defect 67.9%, HAM10000 70.73%) on the public dataset achieved with the proposed model implies that MT-former would be a useful model for classifying the new types of defects from the existing ones.
Dual-modal spatiotemporal imaging of ultrafast dynamics in laser-induced periodic surface structures
Published
, Published online: 14 May 2025
, doi: 10.37188/lam.2025.030
The interactions between ultrafast lasers and materials reveal a range of nonlinear transient phenomena that are crucial in advanced manufacturing. Understanding these interactions during ultrafast laser ablation requires detailed measurements of material properties and structural changes with high temporal and spatial resolutions. Traditional spatiotemporal imaging techniques relying on reflective imaging often fail to capture comprehensive information, resulting in predominantly qualitative theoretical models of these interactions. To overcome this limitation, we propose a dual-modal ultrafast microscopy system that combines two-dimensional reflectivity and three-dimensional topography imaging. By integrating pump-probe techniques with an interferometric imaging system, impressive spatiotemporal resolutions of 236 nm and 256 fs were achieved. Furthermore, using this system, we successfully examined the dynamics of laser-induced periodic surface structure formation, strengthening, and erasure on Si surfaces. The results demonstrate that the dual-modal spatiotemporal imaging technique can serve as a robust tool for the comprehensive analysis of ablation dynamics, facilitating a deeper understanding of the fundamental physics involved and enabling more accurate optimisation of ultrafast laser fabrication processes.
Published
, Published online: 13 May 2025
, doi: 10.37188/lam.2025.035
Surface-enhanced Raman spectroscopy (SERS) is a promising analytical method for studying the structure and composition of multi-component media in clinical practice. However, the practical application of SERS imposes several conditions and restrictions depending on the required analytical accuracy and implementation complexity. This paper proposes a simple serum SERS technique for diagnosing multiple myeloma (MM). The process utilises a substrate composed of agglomerated spherical silver particles (~200 nm) with a capillary effect on sodium nitrate crystals (0.02% volume concentration), excited at a wavelength of 785 nm. Spectral characteristics were recorded using a detector without external cooling, ensuring a cost-effective approach. The proposed low-cost approach was tested on 31 samples from MM and 102 serum samples from an age-matched control group. Combining the SERS technique with the multivariate analysis for serum testing provided high discrimination rates for MM patients compared to the control patients, with an average accuracy of over 96%. Biochemical interpretation of the recorded spectra identified the informative bands at 635 cm-1, 723 cm-1, and 1052 cm-1. Overall, the proposed SERS-based tool can become the basis for screening for MM and can be easily integrated into clinical practice, expanding diagnostic capabilities where expensive laboratory techniques are not affordable.
Published
, Published online: 09 May 2025
, doi: 10.37188/lam.2025.031
Mode division multiplexing (MDM) using multimode fibers (MMFs) is key to meeting the demand for higher data rates and advancing internet technologies. However, optical transmission within MMFs presents challenges, particularly due to mode crosstalk, which complicates the use of MMFs to increase system capacity. Quantitatively analyzing the output of MMFs is essential not only for telecommunications but also for applications like fiber sensors, fiber lasers, and endoscopy. With the success of deep neural networks (DNNs), AI-driven mode decomposition (MD) has emerged as a leading solution for MMFs. However, almost all implementations rely on Graphics Processing Units (GPUs), which have high computational and system integration demands. Additionally, achieving the critical latency for real-time data transfer in closed-loop systems remains a challenge. In this work, we propose using field-programmable gate arrays (FPGAs) to perform neural network inference for MD, marking the first use of FPGAs for this application, which is important, since the latency of closed-loop control could be significantly lower than at GPUs. A convolutional neural network (CNN) is trained on synthetic data to predict mode weights (amplitude and phase) from intensity images. After quantizing the model’s parameters, the CNN is executed on an FPGA using fixed-point arithmetic. The results demonstrate that the FPGA-based neural network can accurately decompose up to six modes. The FPGA’s customization and high efficiency provide substantial advantages, with low power consumption (2.4 Watts) and rapid inference (over 100 Hz), offering practical solutions for real-time applications. The proposed FPGA-based MD solution, coupled with closed-loop control, shows promise for applications in fiber characterization, communications, and beyond.
Published
, Published online: 11 April 2025
, doi: 10.37188/lam.2025.029
Monolithic multi-freeform optical structures play significant roles in advanced optical systems by simplifying system structures and enhancing optoelectronic performance. However, manufacturing and measurement present significant challenges, which require the simultaneous assurance of form quality and relative positioning of multiple functional surfaces. Consequently, a deterministic form-position deflectometric measuring method is proposed based on Bayesian multisensor fusion, which effectively overcomes the inherent limitation of deflectometry in absolute positioning. Calibration priors were marginalised in the measurement model to improve fidelity, and a fully probabilistic measurement framework was proposed to eliminate numerical bias in conventional sequential optimisation approaches. Finally, a geometric-constraint-based registration method was developed to evaluate the form-position quality of freeform surfaces. The experimental results demonstrated the measurement accuracy could achieve a level of one hundred nanometres for surface forms and a few microns for surface positions.
Published
, Published online: 03 April 2025
, doi: 10.37188/lam.2025.021
Light-based additive manufacturing holds great potential in the field of bioprinting due to its exceptional spatial resolution, enabling the reconstruction of intricate tissue structures. However, printing through biological tissues is severely limited due to the strong optical scattering within the tissues. The propagation of light is scrambled to form random speckle patterns, making it impossible to print features at the diffraction-limited size with conventional printing approaches. The poor tissue penetration depth of ultra-violet or blue light, which is commonly used to trigger photopolymerization, further limits the fabrication of high cell-density tissue constructs. Recently, several strategies based on wavefront shaping have been developed to manipulate the light and refocus it inside scattering media to a diffraction-limited spot. In this study, we present a high-resolution additive manufacturing technique using upconversion nanoparticles and a wavefront shaping method that does not require measurement from an invasive detector, i.e., it is a non-invasive technique. Upconversion nanoparticles convert near-infrared light to ultraviolet and visible light. The ultraviolet light serves as a light source for photopolymerization and the visible light as a guide star for digital light shaping. The incident light pattern is manipulated using the feedback information of the guide star to focus light through the tissue. In this way, we experimentally demonstrate that near-infrared light can be non-invasively focused through a strongly scattering medium. By exploiting the optical memory effect, we further demonstrate micro-meter resolution additive manufacturing through highly scattering media such as a 300-μm-thick chicken breast. This study provides a concept of high-resolution additive manufacturing through turbid media with potential application in tissue engineering.
Published
, Published online: 19 March 2025
, doi: 10.37188/lam.2025.017
Complex field modulation (CFM) has found a plethora of applications in physics, biomedicine, and instrumentation. Among existing methods, superpixel-based CFM has been increasingly featured because of its advantages in high modulation accuracy and its compatibility with high-speed spatial light modulators (SLMs). Nonetheless, the mainstream approach based on binary-amplitude modulation confronts limitations in optical efficiency and dynamic range. To surmount these challenges, we develop binary phase-engraved (BiPE) superpixel-based CFM and implement it using the phase light modulator (PLM)—a new micro-electromechanical system-based SLM undergoing development by Texas Instruments in recent years. Using BiPE superpixels, we demonstrate high-accuracy spatial amplitude and phase modulation at up to 1.44 kHz. To showcase its broad utility, we apply BiPE-superpixel-based CFM to beam shaping, high-speed projection, and augmented-reality display.
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