用自监督模型让机器人高精度自动测半导体性能，24小时测125次以上。
A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties
- 用视觉自监督CNN优化接触点定位，精度提升20%。
- 设计图规划算法，测量速度提升6倍，规划波动减少6倍。
- 适合做半导体材料自动化表征的研究者和工业界应用。
将机器人驱动的接触式材料表征技术融入自动驾驶实验室，可提升测量质量、可靠性和吞吐量。尽管深度学习支持自主性，但现有方法在像素级定位精度上不足，且依赖大量标注数据。为此，我们提出一种自监督自主系统，使机器人在高通量下遵循领域专家的测量原则。首先，设计基于视觉的自监督卷积神经网络（CNN），利用可微图像先验优化特定目标，使预测接触位置的像素精度相对现有方法提高20.0%。其次，设计可靠的图规划算法，生成最短路径，使测量吞吐量加速，并将规划方差降低6倍。我们通过4自由度机器人探针连续运行24小时，对梯度滴铸钙钛矿薄膜在3,025个独立预测位点进行光电导率表征，实现每小时超125次测量。空间映射结果揭示了组分趋势与不均一区域，有助于识别制造缺陷。该自监督CNN驱动的机器人系统实现了高精度、高通量、可靠的接触式表征自动化，使此前难以获取的重要半导体特性得以测量，推动自动驾驶实验室发展。
原文摘要 · Abstract (English)
Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high-throughputs. Firstly, we design a vision-based, self-supervised convolutional neural network (CNN) architecture that uses differentiable image priors to optimize domain-specific objectives, refining the pixel precision of predicted robot contact poses by 20.0% relative to existing approaches. Secondly, we design a reliable graph-based planner for generating distance-minimizing paths to accelerate the robot measurement throughput and decrease planning variance by 6x. We demonstrate the performance of this approach by autonomously driving a 4-degree-of-freedom robotic probe for 24 hours to characterize semiconductor photoconductivity at 3,025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs over 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing process defects. With this self-supervised CNN-driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing the measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.
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