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Nature Machine Intelligence

  • Perovskite

Self-supervised deep learning for tracking degradation of perovskite light-emitting diodes with multispectral imaging

Authors Kangyu Ji, Weizhe Lin, Yuqi Sun, Lin-Song Cui, Javad Shamsi, Yu-Hsien Chiang, Jiawei Chen, Elizabeth M. Tennyson, Linjie Dai, Qingbiao Li, Kyle Frohna, Miguel Anaya, Neil C. Greenham & Samuel D. Stranks


Emerging functional materials such as halide perovskites are intrinsically unstable, causing long-term instability in optoelectronic devices made from these materials. This leads to difficulty in capturing useful information on device degradation through time-consuming optical characterization in their operating environments. Despite these challenges, understanding the degradation mechanism is crucial for advancing the technology towards commercialization. Here we present a self-supervised machine learning model that utilizes a multi-channel correlation and blind denoising to recover images without high-quality references, enabling fast and low-dose measurements. We perform operando luminescence mapping of various emerging optoelectronic semiconductors, including organic and halide perovskite photovoltaic and light-emitting devices. By tracking the spatially resolved degradation in electroluminescence of mixed-halide perovskite blue-light-emitting diodes, we discovered that lateral ion migration (perpendicular to the external electric field) during device operation triggers the formation of chloride-rich defective regions that emit poorly—a mechanism that would not be resolvable with conventional imaging approaches.

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