基于机器学习的奖励驱动调整扫描探头显微镜:迈向全自动化显微镜
Yu Liu1, Roger Proksch1,2, Jason Bemis2
1Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States.
ACS nano
|May 19, 2025
概括
自动扫描探针显微镜 (SPM) 使用奖励驱动的工作流的触摸模式优化显著提高了效率和可靠性. 这种方法可以确保在各种样品和探测器中提供一致的高质量图像,减少操作员的时间和潜在的损害.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 表面科学是一门学科.
背景情况:
- 触摸模式 (间歇接触模式) 是扫描探头显微镜 (SPM) 中广泛使用的成像技术.
- 手动优化点击模式耗时,取决于操作人员,并可能导致样品/探头损坏和可重复性差.
- 现有的控制和机器学习方法与触摸模式成像的复杂优化作斗争.
研究的目的:
- 开发一种新的奖励驱动的工作流程,用于自动化优化SPM点击模式.
- 创建一个模拟人类操作员决策的图像质量的样本不可知测量.
- 为了提高点击模式SPM操作的效率,一致性和可靠性.
主要方法:
- 一个奖励驱动的工作流被设计为自动化SPM点击模式参数优化.
- 开发了一个奖励函数,结合了扫描质量的物理和经验知识.
- 工作流被测试在不同的探测器和样品类型.
主要成果:
- 自动化工作流成功地确定了触摸模式的最佳扫描参数.
- 该系统在各种探测器和样本中产生一致的高质量图像.
- 奖励函数提供了对图像质量的样本无关测量.
结论:
- 开发的奖励驱动工作流提供了一种高效可靠的方法来优化SPM点击模式.
- 这种自动化减少了手动调整的需要,节省了时间并最大限度地减少了损坏.
- 该方法显示了提高新用户和复杂样本的SPM可用性的潜力.
关键词:
贝叶斯优化的贝叶斯优化SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM SPM积极学习是积极学习.自动化实验自动化实验智能自动化智能自动化由奖励驱动的奖励驱动.触摸模式 触摸模式更多相关视频
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