Integrating autonomous contact-based robotic characterization into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose a self-supervised convolutional neural network with a spatially differentiable loss function, incorporating shape priors to refine predictions of optimal robot contact poses for semiconductor characterization. This network improves valid pose generation by 20.0%, relative to existing models. We demonstrate our network's performance by driving a 4-degree-of-freedom robot to characterize photoconductivity at 3,025 predicted poses across a gradient of perovskite compositions, achieving throughputs over 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals regions of inhomogeneity. With this self-supervised deep learning-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.

翻译：将自主接触式机器人表征技术集成到自动驾驶实验室中，能够提升测量质量、可靠性和通量。尽管深度学习模型能够支持稳健的自主操作，但现有方法缺乏像素级精确定位能力，且需要大量标注数据。为克服这些挑战，我们提出一种具有空间可微分损失函数的自监督卷积神经网络，该网络结合形状先验知识，以优化针对半导体表征的最佳机器人接触位姿预测。相较于现有模型，该网络将有效位姿生成率提升了20.0%。我们通过驱动一个4自由度机器人在梯度钙钛矿成分样本的3,025个预测位姿上进行光电导表征，验证了网络的性能，实现了每小时超过125次测量的通量。将光电导性空间映射至每片滴铸薄膜上，揭示了材料的非均匀性区域。借助这一自监督深度学习驱动的机器人系统，我们实现了高通量下接触式表征技术的高精度可靠自动化，从而使自动驾驶实验室能够测量以往难以获取却至关重要的半导体特性。