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Data centers are growing in both size and complexity. Keeping track of thousands of servers, cables, and rack components through manual inspections is slow, inconsistent, and leaves gaps where problems can go unnoticed. AI-powered machine vision systems address this by monitoring rack conditions continuously by detecting issues as they happen rather than during the next scheduled check. This application note covers: Identifying and verifying rack components automatically Detecting problems like disconnected cables or missing hardware Reading labels, port IDs, and asset tags and checking them against your configuration records Deploying inspection across multiple racks, aisles, and facilities at scale.

半导体制造是世界上最先进且要求极高的生产流程之一。如今，芯片将数十亿个晶体管封装到微观结构中，制造过程的每一步都必须以绝对的精度执行。哪怕是最微小的缺陷或对准误差，都可能引发良率损失、晶圆浪费以及数百万美元的价值损失。