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A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties.

Alexander E Siemenn¹, Basita Das¹, Kangyu Ji^1,2

¹Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.

Science Advances

|July 4, 2025

Summary

This study introduces self-supervised robotic autonomy for material characterization, enhancing measurement quality and throughput in self-driving labs. The system achieves high-precision photoconductivity mapping of perovskite films, identifying manufacturing defects.

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Area of Science:

Materials Science
Robotics
Artificial Intelligence

Background:

Integrating robotic systems into self-driving laboratories can improve material characterization.
Current deep learning methods for robotic autonomy lack precise positioning and require extensive labeled data.

Purpose of the Study:

To develop self-supervised autonomy for contact-based robotic systems in material characterization.
To enhance measurement quality, reliability, and throughput in self-driving laboratories.

Main Methods:

Implemented a self-supervised neural network-driven robotic system with a 4-DOF robotic probe.
Autonomously characterized semiconductor photoconductivity across perovskite film compositions for 24 hours.
Achieved pixel-precision positioning and followed expert measurement principles.

Main Results:

Successfully characterized photoconductivity at 3025 unique poses, exceeding 125 measurements per hour.
Spatially mapped photoconductivity, revealing compositional trends and inhomogeneities in perovskite films.
Demonstrated high-precision and reliable automation of contact-based characterization.

Conclusions:

Self-supervised autonomy enables high-throughput, precise material characterization in robotic systems.
This approach facilitates the identification of manufacturing defects and the study of critical semiconductor properties.
The system advances the capabilities of self-driving laboratories for advanced material analysis.