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DE-SC0013957: Machine-Learning-Enabled Microscopy to Probe Charge Dynamics at Semiconductor Surfaces for Photovoltaic and Quantum Light Applications

Award Status: Active
  • Institution: University of Washington, Seattle, WA
  • UEI: HD1WMN6945W6
  • PM: Zhu, Jane
  • Most Recent Award Date: 07/29/2026
  • Number of Support Periods: 12
  • PI: Ginger, David
  • Current Budget Period: 07/15/2026 - 07/14/2027
  • Current Project Period: 07/15/2024 - 07/14/2027
 

Public Abstract

This project will develop and apply new scanning probe microscopy methods that fuse hardware and software developments to realize tools capable of probing halide perovskite semiconductor interfaces and devices under operating conditions in working solar cells at timescales significantly faster than that has previously been achieved under similar operating geometries of full solar cell stacks. The ability to probe local photoinduced carrier dynamics below the diffraction limit is a basic science challenge that would advance the understanding of many new semiconductors with technological applications ranging from solar energy harvesting to new sources of quantum light. This project will advance this broad science goal with specific application to questions in halide perovskites by advancing machine learning (ML)-based data analysis and signal processing, with the goal of improving signal to noise to approach “single pulse” (rather than time averaged) dynamics. The project will design and build a new sample cell to enable dynamic scanning probe measurements on cross-sectioned devices under illumination from a fiber-coupled pulsed broad-band light source. Finally, the project will adapt and integrate state-of-the-art optical excitation and fast demodulation methods with modular external electronics, allowing their portable application on multiple new microscopes, including a new ultrahigh vacuum scanning probe system. The project will apply these methods to understand halide perovskite surface passivation and interface charge dynamics under operating conditions in both partial and full solar cell stacks with complete interfaces. The resulting time-resolved imaging (movies) of electronic and ionic carrier motion under illumination with different surface passivation and interfacial layers will produce knowledge to directly improve the operational stability of the current generation of perovskite solar cells, while also helping understand basic science problems that impact the use of perovskite semiconductors in applications ranging from radiation detectors to light-emitting diodes, to sources of quantum light for next generation quantum communication and computing applications.

 



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