A self-supervised, spatially differentiable CNN chooses probe poses on images of perovskite films, a noisy Dijkstra planner routes the robot, and the system autonomously maps photoconductivity across 35 film compositions at over 125 measurements per hour.
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A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties
A self-supervised, spatially differentiable CNN chooses probe poses on images of perovskite films, a noisy Dijkstra planner routes the robot, and the system autonomously maps photoconductivity across 35 film compositions at over 125 measurements per hour.