The political history of China 1840-1928

During the past decade general readers as well as college students have given increasingly close attention to Far Eastern hhistory. For either the formal or the informal student in this the field.xii, 545 p.; 24 c

The political history of China 1840-1928

During the past decade general readers as well as college students have given increasingly close attention to Far Eastern hhistory. For either the formal or the informal student in this the field.xii, 545 p.; 24 c

The political history of China 1840-1928

During the past decade general readers as well as college students have given increasingly close attention to Far Eastern hhistory. For either the formal or the informal student in this the field.xii, 545 p.; 24 c

A Self-Training-Based System for Die Defect Classification

With increasing wafer sizes and diversifying die patterns, automated optical inspection (AOI) is progressively replacing traditional visual inspection (VI) for wafer defect detection. Yet, the defect classification efficacy of current AOI systems in our case company is not optimal. This limitation is due to the algorithms’ reliance on expertly designed features, reducing adaptability across various product models. Additionally, the limited time available for operators to annotate defect samples restricts learning potential. Our study introduces a novel hybrid self-training algorithm, leveraging semi-supervised learning that integrates pseudo-labeling, noisy student, curriculum labeling, and the Taguchi method. This approach enables classifiers to autonomously integrate information from unlabeled data, bypassing the need for feature extraction, even with scarcely labeled data. Our experiments on a small-scale set show that with 25% and 50% labeled data, the method achieves over 92% accuracy. Remarkably, with only 10% labeled data, our hybrid method surpasses the supervised DenseNet classifier by over 20%, achieving more than 82% accuracy. On a large-scale set, the hybrid method consistently outperforms other approaches, achieving up to 88.75%, 86.31%, and 83.61% accuracy with 50%, 25%, and 10% labeled data. Further experiments confirm our method’s consistent superiority, highlighting its potential for high classification accuracy in limited-data scenarios

Solid Phase Epitaxy of Single Phase Two-Dimensional Layered InSe Grown by MBE

Single-phase two-dimensional (2D) indium monoselenide (γ-InSe) film is successfully grown via solid phase epitaxy in the molecular beam epitaxy (MBE) system. Having high electron mobility and high photoresponsivity, ultrathin 2D γ-InSe semiconductors are attractive for future field-effect transistor and optoelectronic devices. However, growing single-phase γ-InSe film is a challenge due to the polymorphic nature of indium selenide (γ-InSe, α-In2Se3, β-In2Se3, γ-In2Se3, etc.). In this work, the 2D α-In2Se3 film was first grown on a sapphire substrate by MBE. Then, the high In/Se ratio sources were deposited on the α-In2Se3 surface, and an γ-InSe crystal emerged via solid-phase epitaxy. After 50 min of deposition, the initially 2D α-In2Se3 phase was also transformed into a 2D γ-InSe crystal. The phase transition from 2D α-In2Se3 to γ-InSe was confirmed by Raman, XRD, and TEM analysis. The structural ordering of 2D γ-InSe film was characterized by synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS)