Deep learning robotics using self-supervised spatial differentiation drive autonomous contact-based semiconductor characterization

• URL: http://arxiv.org/abs/2411.09892v1
• Date: Fri, 15 Nov 2024 02:36:36 GMT
• Title: Deep learning robotics using self-supervised spatial differentiation drive autonomous contact-based semiconductor characterization
• Authors: Alexander E. Siemenn, Basita Das, Kangyu Ji, Fang Sheng, Tonio Buonassisi,
• Abstract summary: We propose a self-supervised convolutional neural network with a spatially differentiable loss function. 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.
• Score: 40.361306070887366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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