Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV24)
Publication date: 6th February 2024
The rapid evolution of technologies demands innovative approaches for electronic devices to enhance their performance and energy efficiency. Halide perovskite memristors have emerged as promising candidates due to their unique properties and potential applications in neuromorphic computing. In this presentation, we delve into the intricate mechanism governing the behavior of halide perovskite memristors, aiming to unravel the underlying principles that dictate their operation.
To evaluate the mechanism responsible for the resistance changes observed in halide perovskite memristors we conducted different optical and electrical measurements, including voltage-current transients, conductive atomic force microscopy (C-AFM), and photoluminescence mapping (PL‑mapping). By employing this combination of experimental techniques, we inspected the structural and electronic dynamics within the halide perovskite material, seeking to elucidate the fundamental processes driving the memristive behavior.
Furthermore, we have modeled this type of memristor theoretically by making simple considerations. This has allowed us to describe this system mathematically with simple equations. By developing these equations we were able to reproduce the electrical responses obtained experimentally. We will present this model in this talk emphasizing that its application can go beyond the memristors studied in this work. We believe that this model could be applied to other memristors based on halide perovskite and even other materials.
This presentation contributes to the growing and required knowledge of halide perovskite memristors, providing a deeper understanding of their operational principles and paving the way for the development of more efficient and versatile electronic devices.
The authors acknowledge the received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 947221.
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