Publication date: 15th December 2025
The era of Big Data and Artificial Intelligence (AI) is generating huge amounts of data daily, posing a significant challenge for conventional computing technologies, which require high power consumption for processing. Neuromorphic computing is emerging as a new computing paradigm that offers a more efficient solution since it mimics the structure and function of the human brain, the most energy-efficient computing system known. Neuromorphic hardware requires electronic elements that emulate the synapses and other neural processes. To this end, Pb-based halide perovskite (Pb-HP) memristors are excellent candidates due to their outstanding properties, but the toxicity of Pb hinders their practical application. For the development of the technology, it is urgent to find Pb-free alternative materials that enable the fabrication of high-performance memristors.
In this presentation, we will analyze the potential of diverse Pb-free perovskite-inspired materials (based on Bi, Cu, and Zn) to fabricate efficient memristors.[1] We will discuss several strategies to enhance the reliability and energy efficiency of the devices, such as the use of inorganic and organic interfacial buffer layers.[2] To demonstrate the effectiveness of these strategies, we investigate the resistive switching properties and the synaptic plasticity of the memristors. Overall, our results highlight the potential of Pb-free perovskite-inspired materials to fabricate efficient memristors for next-generation sustainable neuromorphic computing.
I. Sanjuán acknowledges funding from the Marie Skłodowska-Curie Actions Postdoctoral Fellowship (project MemSusPer, Grant Agreement No. 101207139) under the Horizon Europe research and innovation programme.
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