显微镜知识库

徕卡显微系统的知识库提供有关显微镜学科的科学研究和教学材料。内容旨在对显微镜初学者、有经验的显微镜操作实践者和使用显微镜的科学家在他们的日常工作和实验有所帮助。这里有探索交互式教程和应用笔记，你可以找到你需要的显微镜的基础知识以及前沿技术——快来加入徕卡显微知识社区，分享您的专业知识！

Particle analysis with the Visoria M microscope

AI-Based Particle Counting with the Aivaro AI Feature Detection Software Module

The Aivaro AI Feature Detection software module enables fast manual microscope inspection of particulate contamination on various material samples. By training different classes, reflective particles,…

AI-based inspection detects, classifies, and measures defects directly in microscope images of components.

AI Image Analysis for Reliable Defect Detection

AI-based visual inspection with microscopy helps make quality control in industrial environments efficient by reducing manual inspection effort concerning defect detection, classification, and…

An Overview of Artificial Intelligence (AI) and Machine Learning

AI is rapidly changing industrial inspection, quality control, and manufacturing workflows. But not all AI technologies work the same way. From machine learning and deep learning to the newer concept…

Why the Future of Microscopic Inspection Is AI-Assisted, Not Fully Automated

The next wave of QC productivity gains will not come from building more automated inspection lines. Instead, it will come from augmenting millions of inspection decisions that are made manually every…

Optical microscope image, which is a composition of both brightfield and fluorescence illumination, showing organic contamination on a wafer surface. The inset images in the upper left corner show the brightfield image (above) and fluorescence image (below with dark background).

晶圆表面光刻胶残留与有机污染物可视化检测

随着半导体集成电路 (IC) 的尺寸缩小到 10 纳米以下，在晶圆检测过程中有效检测光刻胶残留物等有机污染物和缺陷变得越来越重要。光学显微镜仍是常用的检测方法，但对于有机污染而言，明视野和其他类型的照明都有其局限性。本文讨论了在半导体行业的质量控制、故障分析和研发&D 过程中，如何利用荧光显微镜有效检测晶片上的光刻胶残留物和其他有机污染物。

无需用手接触即可安全装载晶片，进行显微镜检测

本文介绍了用于显微镜检测的自动硅晶片装载如何帮助改进微电子工艺控制和生产效率。人工搬运晶圆很可能会损坏脆弱的晶圆表面，从而增加成本，而自动化搬运则能确保更安全、更具成本效益的生产。自动晶片装载机在显微镜检测和制造方面的优势概述如下。

Image of burrs (red arrows) at the edge of a battery electrode acquired with a DVM6 digital microscope.

电池制造过程中的毛刺检测

毛刺是电池电极片边缘可能出现的缺陷，例如在制造过程中的分切环节。它们可能会因诸如短路等故障导致电池性能下降，并引发安全和可靠性问题。毛刺检测是电池生产质量控制的重要部分，对于生产具有可靠性能和寿命的电池至关重要。通过适当照明的光学显微镜可以在生产过程的关键步骤中快速可靠地对电极上的毛刺进行视觉检测。

Quality assurance during production in a manufacturing plant.

跨行业的质量保证改进

精确是最重要的。试想一下，心脏起搏器在运行过程中发生故障，或者半导体缺陷导致关键系统崩溃。在医疗设备、电子产品和半导体等行业，误差几乎为零。质量保证（QA）不再仅仅是一项监管要求，而是一项推动业务成功和保护品牌完整性的战略优势。

Image of a Siemens star, where the diameter of the 1st black line circle is 10 mm and the 2nd is 20 mm, taken via an eyepiece of a M205 A stereo microscope. The rectangles represent the field of view (FOV) of a Leica digital camera when installed with various C-mounts (red 0.32x, blue 0.5x, green 0.63x).

30000:1放大率到底意味着什么？

关于光学显微镜性能的一个重要标准是放大率。本报告将为数字显微镜用户提供有用的指南，以确定放大率值的有用范围。