Search Results (417)

With the increasing demand to scale the chip industry, attention is turning to the vital role that phosphanes and silanes play in semiconductor manufacturing processes such as chemical vapor deposition, plasma etching, and impurity doping. High-performance semiconductors often require a supply of ultra-pure gaseous phosphine (≥99.999%) to ensure the formation of defect-free thin-film structures with high integrity and strong functionality. In recent years, research on high-purity PH₃ synthesis methods has mainly focused on two pathways: the acidic route with fewer side reactions, high by-product economics, and higher exergy of high-purity PH₃, and the alkaline alternative with greater potential for practical application through lower reaction temperatures and a simpler reaction process. This paper presents the first comparative study and analysis on the preparation of ultra-high-purity PH₃ and its process energy consumption. Using Aspen and its related software, the energy consumption and cost issues are discussed, and the process heat exchange network is established and optimised. By combining Aspen Plus V14 with MATLAB 2023, an artificial neural network (ANN) prediction model is established, and the parameters of the distillation section equipment are optimised through the NSGA-II model to solve problems such as low product yield and large equipment exergy loss. After optimisation, it can be found that in terms of energy consumption and cost indicators, the acidic process has greater advantages in large-scale production of high-purity PH₃. The total energy consumption of the acidic process is 1.6 × 10⁸ kJ/h, which is only one-third that of the alkaline process, while the cost of the heat exchange equipment is approximately three-quarters that of the alkaline process. Through dual-objective optimisation, the exergy loss of the acidic distillation part can be reduced by 1714.1 kW, and the economic cost can be reduced by USD 3673. Therefore, from the perspective of energy usage and equipment manufacturing, the comprehensive analysis of the acidic process has more advantages than that of the alkaline process. Full article

With the development of the semiconductor manufacturing process towards miniaturization and high integration, the detection of microscopic defects on wafer surfaces faces the challenge of balancing precision and efficiency. Therefore, this study proposes a lightweight inspection model based on the YOLOv8 framework, aiming to achieve an optimal balance between inspection accuracy, model complexity, and inference speed. First, we design a novel lightweight module called IRB-GhostConv-C2f (IGC) to replace the C2f module in the backbone, thereby significantly minimizing redundant feature computations. Second, a CNN-based cross-scale feature fusion neck network, the CCFF-ISC neck, is proposed to reduce the redundant computation of low-level features and enhance the expression of multi-scale semantic information. Meanwhile, the novel IRB-SCSA-C2f (ISC) module replaces the C2f in the neck to further improve the efficiency of feature fusion. In addition, a novel dynamic head network, DyHeadv3, is integrated into the head structure, aiming to improve the small-scale target detection performance by dynamically adjusting the feature interaction mechanism. Finally, so as to comprehensively assess the proposed algorithm’s performance, an industrial dataset of wafer defects, WSDD, is constructed, which covers “broken edges”, “scratches”, “oil pollution”, and “minor defects”. The experimental results demonstrate that the CISC-YOLO model attains an mAP50 of 93.7%, and the parameter amount is reduced to 1.92 M, outperforming other mainstream leading algorithms in the field. The proposed approach provides a high-precision and low-latency real-time defect detection solution for semiconductor industry scenarios. Full article

In advanced manufacturing, nanotechnology, and aerospace fields, the demand for precision is increasing. Driven by this demand, multi-degree-of-freedom grating encoders have become particularly crucial in high-precision displacement and angle measurement. Over the years, these encoders have evolved from one-dimensional systems to complex multi-degree-of-freedom measurement solutions that can achieve real-time synchronization. There can also be high-resolution feedback. Its structure is relatively compact, the signal output is also very stable, and the integration degree is high. This gives it a significant advantage in complex measurement tasks. Recently, there have been new developments. The functions of grating encoders in terms of principle, system architecture, error modeling, and signal processing strategies have all been expanded. For instance, accuracy can be improved by integrating multiple reading-heads, while innovative strategies such as error decoupling and robustness enhancement have further advanced system performance. This article will focus on the development of two-dimensional, three-dimensional and multi-degree-of-freedom grating encoders, exploring how the measurement degrees of freedom have evolved, and emphasizing key developments in spatial decoupling, error compensation and system integration. At the same time, it will also discuss some challenges, such as error coupling, system stability and intelligent algorithms for integrating real-time error correction. The future of grating encoders holds great potential. Their applications in precision control, semiconductor calibration, calibration systems, and next-generation intelligent manufacturing technologies can bring promising progress to both industrial and scientific fields. Full article

Silicon carbide (SiC) is a promising semiconductor material for electronics and photonics. Ultrafast laser processing of SiC enables three-dimensional nanostructuring, enriching and expanding the functionalities of SiC devices. However, challenges arise in delivering uniform, high-aspect-ratio (length-to-width) nanostructures due to difficulties in confining light energy at the nanoscale while simultaneously regulating intense photo modifications. In this study, we report the controllable growth of long-distance, high-straightness, and high-parallelism multifilament structures in SiC using ultrafast laser processing. The mechanism is the formation of femtosecond multifilaments through the nonlinear effects of clamping equilibrium, which allow highly confined light to propagate without diffraction in parallel channels, further inducing high-aspect-ratio nanostripe-like photomodifications. By employing an elliptical Gaussian beam—rather than a circular one—and optimizing pulse durations to stabilize multifilaments with regular positional distributions, the induced multifilament structures can reach a length of approximately 90 μm with a minimum linewidth of only 28 nm, resulting in an aspect ratio of over 3200:1. Raman tests indicate that the photomodified regions consist of amorphous SiC, amorphous silicon, and amorphous carbon, and photoluminescence tests reveal that silicon vacancy color centers could be induced in areas with lower light power density. By leveraging femtosecond multifilaments for diffraction-less light confinement, this work proposes an effective method for manufacturing deep-subwavelength, high-aspect-ratio nanostructures in SiC. Full article

In order to solve the problem of the efficiency reduction and complex manufacturing of traditional grating couplers under environmental temperature fluctuations, a Si₃N₄ high-efficiency grating coupler integrating a distributed Bragg reflector (DBR) and thermo-optical tuning layer is proposed. In this paper, the double-layer DBR is used to make the down-scattered light interfere with other light and reflect it back into the waveguide. The finite difference time domain (FDTD) method is used to simulate and optimize the key parameters such as grating period, duty cycle, incident angle and cladding thickness, achieving a coupling efficiency of −1.59 dB and a 3 dB bandwidth of 106 nm. In order to further enhance the temperature stability, the amorphous silicon (a-Si) thermo-optical material layer and titanium metal serpentine heater are embedded in the DBR. The reduction in coupling efficiency caused by fluctuations in environmental temperature is compensated via local temperature control. The simulation results show that within the wide temperature range from −55 °C to 150 °C, the compensated coupling efficiency fluctuation is less than 0.02 dB, and the center wavelength undergoes a blue shift. This design is compatible with complementary metal-oxide-semiconductor (CMOS) processes, which not only simplifies the fabrication process but also significantly improves device stability over a wide temperature range. This provides a feasible and efficient coupling solution for photonic integrated chips in non-temperature-controlled environments, such as optical communications, data centers, and automotive systems. Full article

We developed a low-particle emission linear atmospheric plasma device for hydrophilizing silicon wafers, aiming to improve semiconductor manufacturing processes. The device generates a stable plasma curtain using argon or helium gas under specific frequency and power conditions, enabling large-area surface treatment without causing damage. Experimental results demonstrated uniform hydrophilization, characterized by a substantial reduction in water contact angle and minimal particle emission, outperforming conventional jet-type plasma systems. TOF-SIMS analysis confirmed the absence of metal contamination, validating the device’s cleanliness. This technology offers a promising alternative to wet chemical treatments, contributing to environmentally friendly and efficient wafer bonding processes. Full article

Accurate classification of defects in the semiconductor manufacturing process is critical for improving yield and ensuring quality. While previous works have mainly focused on improving classification accuracy, we propose a model that can simultaneously assess accuracy, prediction confidence, and interpretability in wafer defect classification. To solve the class imbalance problem, we used a weighted cross-entropy loss function and convolutional neural network–based model to achieve a high accuracy of 97.8% on the test dataset and applied a temperature-scaling technique to enhance confidence. Furthermore, by simultaneously employing local interpretable model-agnostic explanations and gradient-weighted class activation mapping, the rationale for the predictions of the model was visualized, allowing users to understand the decision-making process of the model from various perspectives. This research can provide a direction for the next generation of intelligent quality management systems by enhancing the applicability of the proposed model in actual semiconductor production sites through explainable predictions. Full article

Improving the transient response performance is a critical challenge in low-dropout regulator (LDO) design. This paper proposes a novel on-chip capacitor-less LDO based on substrate technology implemented in an SMIC (Semiconductor Manufacturing International Corporation) 0.18 μm CMOS (complementary metal oxide semiconductor technology) process. Central to this innovation is a fast response loop between the PMOS driver’s body and gate, which leverages the body effect to enhance driver control without complex bulk-driven techniques. The proposed LDO achieves a quiescent current of 4.5 μA, an efficiency of $88\%$, an overshoot/undershoot of $12\mathrm{mV}/22\mathrm{mV}$, and a settling time of 1.2 μs. The comparative analysis confirms that this structure increases the maximum load current and reduces the loop response time relative to those for conventional LDOs. These results validate a significant improvement in the transient performance, marking an important advance in integrated voltage regulator technology. Full article

The scalable synthesis of high-quality single-crystal diamond films remains pivotal for next-generation extreme-performance devices. Iridium substrates offer exceptional promise for heteroepitaxy, yet early-stage growth mechanisms limiting crystal quality are poorly understood. An integrated multiscale investigation combining first-principles DFT calculations, molecular dynamics simulations, and experimental validation is presented to resolve the oriented attachment process governing diamond growth on Ir(100). Robust interfacial bonding at the interface and optimal carbon coverage are revealed to provide thermodynamic driving forces for primary nucleation. A critical angular tolerance enabling defect-free coalescence through crystallographic realignment is identified by molecular dynamics. Concurrent nucleation growth pathways are experimentally confirmed through SEM, AFM, and Raman spectroscopy, where nascent crystallites undergo spontaneous orientational registry to form continuous epitaxial domains. Grain boundary annihilation is observed upon lattice rotation aligning adjacent grains below the critical angle. Crucially, intrinsic atomic steps are generated on the resultant coalesced layer, eliminating conventional etching requirements for homoepitaxial thickening. This work advances fundamental understanding of single-crystal diamond growth mechanisms, facilitating enhanced quality control for semiconductor device manufacturing and quantum applications. Full article

We developed an artificial intelligence (AI) model to optimize the time efficiency, yield, and energy efficiency of the semiconductor coating process. A random forest-based model was developed for rapid modeling and analysis of the semiconductor coating process, thus allowing designers and operation managers to conduct an efficient and effective process. The developed AI model offers an objective and accurate basis for decision-making, thereby ensuring that each unit is operated energy-efficiently, stably, and reliably in the minimized operation time. The developed model assists Taiwan’s semiconductor industry in transitioning from engineer experience to data-driven approaches, thus accelerating the technological optimization of semiconductor factories and adding value to customers. This model considerably reduces the material, energy, resource, time, labor, and costs of thin film deposition. The model allows the semiconductor industry of Taiwan to consolidate its competitive advantage by achieving net-zero carbon emissions and sustainability. Full article

Glass, as an amorphous material with excellent optical transparency and chemical stability, plays an irreplaceable role in modern engineering and technology fields such as semiconductor manufacturing and micro-electro-mechanical systems (MEMS). For example, borosilicate glass, with a coefficient of thermal expansion (CTE) that is close to having good thermal shock resistance and chemical stability, can be applied to MEMS packaging and aerospace fields. SiO₂ glass exhibits excellent thermal stability, extremely low optical absorption, and high light transmittance, while also possessing strong chemical stability and extremely low dielectric loss. It is widely used in semiconductors, photolithography, and micro-optical devices. However, the stress sensitivity of traditional mechanical joints and the poor weather resistance of adhesive bonding make conventional methods unsuitable for glass joining. Welding technology, with its advantages of high joint strength, structural integrity, and scalability for mass production, has emerged as a key approach for precision glass joining. In the field of glass welding, technologies such as glass brazing, ultrasonic welding, anodic bonding, and laser welding are being widely studied and applied. With the advancement of laser technology, laser welding has emerged as a key solution to overcoming the bottlenecks of conventional processes. This paper, along with the application cases for these technologies, includes an in-depth study of common issues in glass welding, such as residual stress management and interface compatibility design, as well as prospects for the future development of glass welding technology. Full article

Chemical vapor deposition (CVD) is a high-precision thin-film fabrication technique that is widely applied in semiconductor manufacturing, optical component manufacturing, and materials science. The performance of the deposition process plays a critical role in determining the quality of the final product. However, multiple variables in CVD processes have a highly nonlinear nature that involves complex interactions. Therefore, conventional experimental methods exhibit limitations in quality control and process optimization for CVD. In this study, we developed a predictive model based on process parameters and quality indicators using machine learning techniques to analyze and optimize the CVD processes. Through data collection, feature selection, model training, and model validation, the developed machine-learning algorithms were tested and evaluated. The adopted machine learning algorithms effectively captured the nonlinear relationships between multiple variables in CVD processes, accurately predicted thin-film quality indicators, and provided data for optimizing process parameters. In addition, the analysis results of feature importance revealed the effect of each key parameter on product quality, offering a basis for process improvement. Overall, the results of this study highlight the capability of machine learning algorithms for quality control and optimization in CVD processes for future advancements in smart manufacturing. Full article

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields. Full article

Resistive switching (RS) phenomena are nowadays one of the most studied topics in the area of microelectronics. It can be observed in Metal–Insulator–Metal (MIM) structures that are the basis of resistive switching random-access memories (RRAMs). In the case of commercial use of RRAMs, it is beneficial that the applied materials would have to be compatible with Complementary Metal-Oxide-Semiconductor (CMOS) technology. Fabricating methods of these materials can determine their stoichiometry and structural composition, which can have a detrimental impact on the electrical performance of manufactured devices. In this study, we present the influence of the Ar/N₂ ratio during reactive magnetron sputtering of titanium nitride (TiN) electrodes on the resistive switching behavior of MIM devices. We used silicon oxide (SiOx) as a dielectric layer, which was characterized by the same properties in all fabricated MIM structures. The composition of TiN thin layers was controlled by tuning the Ar/N₂ ratio during the deposition process. The fabricated conductive materials were characterized in terms of chemical and structural properties employing X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis. Structural characterization revealed that increasing the Ar content during the reactive sputtering process affects the crystallite size of the deposited TiN layer. The resulting crystallite sizes ranged from 8 Å to 757.4 Å. The I-V measurements of fabricated devices revealed that tuning the Ar/N₂ ratio during the deposition of TiN electrodes affects the RS behavior. Our work shows the importance of controlling the stoichiometry and structural parameters of electrodes on resistive switching phenomena. Full article

The consistent quality control of ultrapure water (UPW) in semiconductor manufacturing depends on removing trace organonitrogen compounds such as urea. Due to its high solubility, chemical stability, and neutral polarity, urea is inadequately removed by conventional processes. Even at low concentrations, it elevates total organic carbon (TOC) and reduces electrical resistivity. The use of reclaimed water as a sustainable feed stream amplifies this challenge because its nitrogen content is variable and persistent. Conventional methods such as reverse osmosis, ultraviolet oxidation, and ion exchange remain limited in treating urea due to its uncharged, low-molecular-weight nature. This review examines the performance and limitations of these processes and explores electrochemical oxidation (EO) as an alternative. Advances in EO are analyzed with attention to degradation pathways, electrode design, reaction selectivity, and operational parameters. Integrated systems combining EO with membrane filtration, adsorption, or chemical oxidation are also reviewed. Although EO shows promise for selectively degrading urea, its application in UPW production is still in its early stages. Challenges such as low conductivity, byproduct formation, and energy efficiency must be addressed. The paper first discusses urea in reclaimed water and associated removal challenges, then examines both conventional and emerging treatment technologies. Subsequent sections delve into the mechanisms and optimization of EO, including electrode materials and operational parameters. The review concludes with a summary of main findings and a discussion of future research directions, aiming to provide a comprehensive foundation for validating EO as a viable technology for producing UPW from reclaimed water. Full article