Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects...Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications.展开更多
Oxide semiconductors(OSs),introduced by the Hosono group in the early 2000s,have evolved from display backplane materials to promising candidates for advanced memory and logic devices.The exceptionally low leakage cur...Oxide semiconductors(OSs),introduced by the Hosono group in the early 2000s,have evolved from display backplane materials to promising candidates for advanced memory and logic devices.The exceptionally low leakage current of OSs and compatibility with three-dimensional(3D)architectures have recently sparked renewed interest in their use in semiconductor applications.This review begins by exploring the unique material properties of OSs,which fundamentally originate from their distinct electronic band structure.Subsequently,we focus on atomic layer deposition(ALD),a core technique for growing excellent OS films,covering both basic and advanced processes compatible with 3D scaling.The basic surface reaction mechanisms—adsorption and reaction—and their roles in film growth are introduced.Furthermore,material design strategies,such as cation selection,crystallinity control,anion doping,and heterostructure engineering,are discussed.We also highlight challenges in memory applications,including contact resistance,hydrogen instability,and lack of p-type materials,and discuss the feasibility of ALD-grown OSs as potential solutions.Lastly,we provide an outlook on the role of ALD-grown OSs in memory technologies.This review bridges material fundamentals and device-level requirements,offering a comprehensive perspective on the potential of ALD-driven OSs for next-generation semiconductor memory devices.展开更多
β-Ga_(2)O_(3)是一种具有超宽带隙、高临界击穿场强和优异的巴利加优值的半导体材料,近年来在电力电子与深紫外光电探测等领域展现出巨大的应用潜力。金属有机化学气相沉积(Metal-organic chemical vapor deposition,MOCVD)技术凭借其...β-Ga_(2)O_(3)是一种具有超宽带隙、高临界击穿场强和优异的巴利加优值的半导体材料,近年来在电力电子与深紫外光电探测等领域展现出巨大的应用潜力。金属有机化学气相沉积(Metal-organic chemical vapor deposition,MOCVD)技术凭借其高生长速率、精确的膜厚控制、优异的薄膜质量和大尺寸生长等优势,成为未来β-Ga_(2)O_(3)走向产业化的潜在方法,并已被广泛应用于β-Ga_(2)O_(3)的外延生长研究。本文对几种常见晶向的β-Ga_(2)O_(3) MOCVD同质外延生长的研究成果进行了概述,并在此基础上介绍了极具潜力的β-(Al_(x)Ga_(1-x))_(2)O_(3)的MOCVD外延生长研究现状。最后,总结了基于MOCVD技术的β-Ga_(2)O_(3)同质外延生长以及β-(Al_(x)Ga_(1-x))_(2)O_(3)生长过程中面临的主要问题,并对未来的发展进行了展望。展开更多
基金funded by the National Science and Technology Council(NSTC),Taiwan,grant number NSTC 114-2218-E-167-001.
文摘Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications.
基金supported by National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(MSIT)(No.RS-2023-00260527,RS-2024-00407282,RS-2025-00557667)supported by Hanyang University Industry-University Cooperation Foundation(No.202400000003943)supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)funded by South Korean Ministry of Trade,Industry and Energy(MOTIE)(No.RS-2025-25454815,RS-2025-02308064,20017382)。
文摘Oxide semiconductors(OSs),introduced by the Hosono group in the early 2000s,have evolved from display backplane materials to promising candidates for advanced memory and logic devices.The exceptionally low leakage current of OSs and compatibility with three-dimensional(3D)architectures have recently sparked renewed interest in their use in semiconductor applications.This review begins by exploring the unique material properties of OSs,which fundamentally originate from their distinct electronic band structure.Subsequently,we focus on atomic layer deposition(ALD),a core technique for growing excellent OS films,covering both basic and advanced processes compatible with 3D scaling.The basic surface reaction mechanisms—adsorption and reaction—and their roles in film growth are introduced.Furthermore,material design strategies,such as cation selection,crystallinity control,anion doping,and heterostructure engineering,are discussed.We also highlight challenges in memory applications,including contact resistance,hydrogen instability,and lack of p-type materials,and discuss the feasibility of ALD-grown OSs as potential solutions.Lastly,we provide an outlook on the role of ALD-grown OSs in memory technologies.This review bridges material fundamentals and device-level requirements,offering a comprehensive perspective on the potential of ALD-driven OSs for next-generation semiconductor memory devices.
文摘β-Ga_(2)O_(3)是一种具有超宽带隙、高临界击穿场强和优异的巴利加优值的半导体材料,近年来在电力电子与深紫外光电探测等领域展现出巨大的应用潜力。金属有机化学气相沉积(Metal-organic chemical vapor deposition,MOCVD)技术凭借其高生长速率、精确的膜厚控制、优异的薄膜质量和大尺寸生长等优势,成为未来β-Ga_(2)O_(3)走向产业化的潜在方法,并已被广泛应用于β-Ga_(2)O_(3)的外延生长研究。本文对几种常见晶向的β-Ga_(2)O_(3) MOCVD同质外延生长的研究成果进行了概述,并在此基础上介绍了极具潜力的β-(Al_(x)Ga_(1-x))_(2)O_(3)的MOCVD外延生长研究现状。最后,总结了基于MOCVD技术的β-Ga_(2)O_(3)同质外延生长以及β-(Al_(x)Ga_(1-x))_(2)O_(3)生长过程中面临的主要问题,并对未来的发展进行了展望。
