Nowadays, demand for the stretchable electronic circuit (SEC) innovative technology is promising in various industries such as biology, advanced robotics, and defense due to their ability to endure enormous amounts of energy and have a wide range of tensile capabilities. However, reliable data regarding mechanical characteristics are still sparse due to the percolation nature of the material, which requires its fillers to be connected to one another at all times in order to conduct electricity. To address these issues, the present work opted a finite element analysis (FEA) model that will depict the behaviour of plastic strain on a variety of stretchy conductive ink designs. SEC ink models were created using CAD modeling software and loaded into ANSYS software for finite element analysis (FEA). Behaviors of six different SEC patterns under various longitudinal and lateral stretching conditions were analyzed using the equivalent plastic strain level possessed. Another perspective of the study was to evaluate the impact of expanding the width and thickness of the ink pattern toward the development of the equivalent plastic strain. The result shows that the U-shape SEC pattern had the lowest equivalent plastic strain, with εp= 0.033611 for the longitudinal load and εp= 0.014648 for the lateral load.

The miniaturization of electronic devices and the consequent decrease in the distance between conductive lines have increased the risk of short circuit failure due to electrochemical migration (ECM). The presence of ionic contaminants affects the ECM process. This work systematically investigates the ECM of tin (Sn) in the presence of bromide ions (Br−) in the range of 10−6 M to 1.0 M. Water drop test (WDT) was conducted in the two-probe semiconductor characterization system under an optical microscope as an in-situ observation. Polarization test was carried out to study the correlation between the corrosion properties of Sn and its ECM behaviour. The products of ECM were characterized by scanning electron microscope coupled with an energy dispersive X-rays spectrometer (SEM/EDX) and X-ray photoelectron spectrometer (XPS). The results confirm that the rate of anodic dissolution of Sn monotonously increases with the Br− concentration. However, the probability of ECM failure follows a normal distribution initially, but later increases with the Br− concentration. The main products of the ECM reactions are identified as Sn dendrites and tin hydroxide precipitates. The mechanisms of the ECM process of Sn in the presence of Br− are also suggested.

Ensuring the quality and functionality of printed circuit boards (PCBs) during manufacturing requires precise, automated visual inspection. Detecting integrated circuits (ICs) on PCBs poses a significant challenge due to diverse component sizes, types, and intricate board markings that complicate accurate object detection. This study addresses this challenge by proposing an enhanced EfficientNet-YOLOv4 algorithm tailored explicitly for the IC detection of PCBs. Numerous modifications are integrated into YOLOv4, with the replacement of its original backbone by a robust feature extraction network, EfficientNetv2-L, and meticulous hyperparameter tuning, including variations in loss functions, anchor size configurations, and other training techniques. The methodology further incorporates diverse data augmentation techniques to enrich the training dataset and enhance the model’s generalization ability. Extensive experiments conducted in this study showed the efficacy and robustness of the algorithm in handling complex PCB layouts and varying lighting conditions, outperforming existing PCB inspection models. The proposed method, EfficientNetv2-L-YOLOv4, achieved an impressive F1-score of 99.22 with an inference speed of 0.14 s per image. The proposed method also performed well compared to EfficientNet-B7-FasterRCNN and the original YOLOv4; it attains an F1-score of 98.96 and an inference speed of 0.10 s per image (with a batch size of 4). These results highlight the significance of effective feature extraction networks for object detection. Beyond addressing IC detection challenges, this algorithm advances the fields of computer vision and object detection. The implementation of EfficientNetv2-L-YOLOv4 in real manufacturing scenarios holds promise for automating component inspections and potentially eliminating the need for human intervention.

Sound waves are a form of energy produced by a vibrating object that travels through the medium that can be heard. Generally, the sound is used in human communication, music, alert, and so on. Furthermore, it also helps us to understand what are the events that occurring in the moment, and thereby, provide us hints to understand what is happening around us. This has prompt researchers to study on how humans understand what event is occurring based on the sound waves. In recent years, researchers also study on how to equip the machine with this ability, i.e. acoustic event detection. This study focuses on the acoustic event detection which leverage both frequency spectrogram technique and deep learning methods. Initially, a spectrogram image is generated from the acoustic data by using the frequency spectrogram technique. Then, the generated frequency spectrogram is fed into a pre-trained MobileNet model to extract robust features representations. In this work, 1 Dimensional Convolutional Neural Network (1D-CNN) is adopted to train a model for acoustic event detection. The feature representations are extracted from a pre-trained MobileNet. The proposed 1D-CNN consist of several alternatives of convolution and pooling layers. The last pooling layer is flattened and fed into a fully connected layer to classify the events. Dropout is employed to prevent overfitting. The proposed frequency spectrogram with pre-trained MobileNet and 1D-CNN is then evaluated with three datasets, which are Soundscapes1, Soundscapes2, and UrbanSound8k. From the experimental results, the proposed method obtained 81, 86, and 70 F1-score, for Soundscapes1, Soundscapes2, and UrbanSound8k, respectively.

This work demonstrates the employment of tungsten trioxide/polydimethylsiloxane nanocomposite saturable absorber (WO3/PDMS-SA) in realizing mode-locked conventional soliton (CS) and noise-like pulse (NLP) laser generation in net anomalous dispersion. The switching formation from CS regime of 970.0 fs pulse duration to NLP regime of 182.0 fs coherent spike with 65.3 ps pedestal was achieved by varying its pump power. The pulse laser exhibited good stability of 50.76 and 49.82 dB signal-to-noise ratio at 9.09 MHz fundamental repetition rate and trivial variation during stability test for CS and NLP regime, respectively. This work expresses the feasibility of WO3/PDMS-SA in attaining various types of mode-locked pulse phenomena using a fixed cavity configuration conceivably beneficial for compact dual-purpose laser systems.

This work demonstrates the fabrication of a saturable absorber with nickel 1,3,5-benzene-tricarboxylic acid (Ni-H3BTC)-metal organic framework (MOF) to generate soliton pulses in an erbium-doped fiber laser. The saturable absorber was fabricated by spin-coating Ni-H3BTC-MOF/polydimethylsiloxane composite on a tapered fiber as the encapsulation agent for light-matter interactions. The proposed cavity emitted a pulse laser at 1557.72 nm central wavelength with 3-dB bandwidth of 5.90 nm, pulse width of 907 fs, and average output power of 13.02 mW. At maximum pump power of 247.8 mW, the pulse energy was calculated to be 1.3 nJ at 10 MHz repetition rate. These results might assist as a footing to explore different types of available MOFs and their properties for the generation of ultrashort pulses.

This work presents a tapered fiber decorated with bulk α-alumina with 4.08% modulation depth and 21.67 MW/cm2 saturation intensity which was responsible to induce noise-like pulse (NLP) in an “eye-safe” wavelength region. The light-matter interaction within evanescent field of ultrathin tapered fiber established NLP in a ring configuration of thulium-doped fiber laser with a broad spectral bandwidth of 45 nm centered at 1948 nm wavelength. The NLP was emitted at 11.36 MHz repetition rate in a net anomalous laser cavity. The generated pulse had a maximum pulse energy of 2.46 nJ at 1587 mW pumping power. The robustness of the NLP performance was validated with 3 h stability test, which opens new possibilities for future research of this material in the wavelength of exceeding 1.9 μm.

A nanocomposite of titania-coated silica saturable absorber (TiO2–SiO2-SA) was fabricated via alkaline fusion and spin-coating. The TiO2–SiO2-SA possessed nonlinear saturable absorption and optical limiting effect with 30.35% modulation depth, 42.57 μJ/cm2 saturation fluence, and 0.0075 cm2/μJ two-photon absorption coefficient. By designing a ring cavity erbium-doped fiber laser in net normal dispersion regime and TiO2–SiO2-SA as mode-locker, a stable noise-like pulse (NLP) at 1.57 μm with ∼21 nm spectral bandwidth was initiated at low threshold power of 22.6 mW, and maintained its operation at maximum pump power of 234 mW. A pulse spike of 587 fs riding on 4.47 ps pulse pedestal at 7.66 MHz repetition frequency confirmed its NLP regime. The generated NLP with TiO2–SiO2-SA was very stable and estimated damage threshold of at least 786 μJ/cm2 pumping fluence suggest its potential as one of next generation nanocomposite materials for ultrafast photonics.