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Abstract Wafer quality control is one of the important processes to improve the yield rate of semiconductor products. Profile quality and defects in the wafer are two key factors that should be taken into consideration. In this research, we introduce a method that measures the profile of the upper surface and the thickness of the wafer at the same time using an optical fiber cascaded Fabry–Pérot interferometer working at wavelength of 1550 nm. Therefore, the 3D profile of the wafer can be reconstructed directly. Testing results show that both accuracy and precision of the Fabry–Pérot interferometer are within a nanometer scale. Defects, especially those embedded inside the wafer, will be detected by monitoring the leaky field with treating wafers as slab waveguides. With the leaky field detection, defects on the lower surface of the wafer were successfully detected by monitoring the leaky field above the upper surface of the wafer. Compared with traditional methods such as radiographic testing or computed tomography testing, the proposed methods provide a cost-effective alternative for wafer quality evaluation. 

Finite element analysis provides visual insights into conductive path evolution in a SiO₂-based memristor. Electrochemical impedance spectroscopy experimentally validated the theoretical findings by interpreting with an equivalent circuit. 

In this research we introduce the application of an optical fiber Fabry-Pérot interferometer in smart manufacturing. We used an optical fiber Fabry-Pérot interferometer to measure the distance between a moving target and a fixed optical fiber. When the target moves, the distance between the fiber and the target can be precisely determined. First, we monitored the distance between a fixed fiber and the surface of a rotating tool. By measuring the distance, we reconstructed the three-dimensional (3D) profile of the tool. We also introduce the method to calculate the runout and tool wear. To further improve the speed of this method, we developed machine learning models to find out the distance from the spectrum of the interferometer since the spectrum analyzing method is relatively slow. It was found that the Deep Neural Network model predicts the distance between the fiber and the target surface with a sufficient precision (< 4 μm) when measuring the straightness error of a computer numerical control (CNC) machine tool. The proposed method provides possibilities for noncontact precise monitoring especially in a limited space. 

Abstract Recent advancements in 3D printing technology have expanded its application to manufacturing pressure sensors. By harnessing the cost‐effectiveness, streamlined processes, and design flexibility of 3D printing, sensor fabrication can be customized to meet specific performance needs. Thus far, 3D printing in pressure sensor development has been primarily limited to creating molds for transferring patterns onto flexible substrates, restricting both material selection and sensor performance. To fully unlock the potential of 3D printing in advanced pressure sensor fabrication, it is crucial to establish effective design rules focused on enhancing the figure of merit performance. This study introduces a universal design strategy aimed at maintaining high sensitivity across a wide pressure range—a challenging feat, as sensitivity significantly decreases at higher pressures. Our approach centers on engineering the deformability of 3D‐printed structures, achieving a linear increase in contact area between sensor patterns and electrodes without reaching saturation. Sensors designed with high elongation and low stiffness exhibit consistent sensitivity of 162.5 kPa⁻¹ across a broad pressure range (0.05–300 kPa). Mechanistic investigations through finite element analysis confirm that engineered deformability is key to achieving this enhanced linear response, offering robust sensing capabilities for demanding applications such as deep‐sea and space exploration.