自主危险气体检测系统:系统性审查
Boon-Keat Chew1, Azwan Mahmud1, Harjit Singh2
1Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本综述探讨了在半导体设施中减少气体检测系统 (GDS) 的手动校准. 先进的分析和机器学习可以纠正传感器漂移,提高安全性和可靠性.
科学领域:
- 工业安全 工业安全 工业安全
- 传感器技术 传感器技术
- 数据分析数据分析数据分析.
背景情况:
- 气体检测系统 (GDS) 对于半导体制造中的危险气体监测至关重要.
- 在GDS中,耗材传感器 (电化学,金属氧化物半导体) 随着时间的推移而降解,导致准确度偏移和交叉灵敏度.
- 手动校准是必不可少的,但资源密集,耗时,容易出错.
研究的目的:
- 系统地审查减少或消除GDS手动校准依赖的方法.
- 探索数据分析和机器学习用于传感器性能校正的应用.
- 研究无校准和自我校正的气体传感器系统的潜力.
主要方法:
- 系统的文献综述 (PROSPERO注册号:1166004) 进行.
- 分析包括主要组件分析 (PCA),支持矢量机器 (SVM) 和多变量回归技术的分析.
- 探索校准传输和传感器数据同步的探索.
主要成果:
- 数据分析和机器学习可以纠正传感器精度漂移,提高气体选择性.
- 像PCA和SVM这样的技术有效地区分目标气体,并弥补老化和环境变化.
- 在整合无校准或自我校正传感器系统方面正在取得进展.
结论:
- 先进的数据分析为GDS的手动校准提供了一个可行的替代方案.
- 关键的挑战包括理解传感器漂移动态和同步多传感器数据.
- 未来的研究应该专注于针对特定应用的数据集,适应性模型和智能GDS的混合验证.
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