Lead-free alternative memristors for sustainable artificial synaptic emulation
Ignacio Sanjuán a
a Institute of Advanced Materials (INAM), University Jaume I, Castellon, Spain
Invited Speaker Session, Ignacio Sanjuán, presentation 018
Publication date: 22nd October 2026

The fast expansion of the Internet of Things (IoT) and Artificial Intelligence (AI) is leading to the daily generation of vast data volumes. The traditional computing technologies require high power consumption to process all this amount of data and data centers entail a significant water footprint due to the cooling systems. Also, data generation is expected to grow exponentially and, therefore, more efficient computing platforms to overcome the limitations of traditional computing are needed. Neuromorphic computing has emerged as a promising candidate. This technology aims to emulate the human brain, which is one of the most energy-efficient information processing systems known. This technology relies on electronic elements to mimic neuronal synapses, enabling communication between artificial neurons.1 Among the different candidates, Pb based halide perovskite memristors stand out due to their excellent coupled ionic-electronic conductivity. However, the toxicity of Pb prevents their potential commercial application. Therefore, it is crucial to find Pb-free alternative materials that enable the fabrication of sustainable high-performance memristors.

In this presentation, we will discuss the potential of Pb-free halide perovskite-inspired materials (based on Bi, Ag, and Zn) and organic mixed ionic electronic conducting polymers (OMIECs)2 for the fabrication of efficient and sustainable memristors. We will introduce strategies to enhance the reliability and energy efficiency of the memristors, such as the use of inorganic and organic interfacial buffer layers.3 To assess the potential of the devices, we first investigate the resistive switching and the synaptic-like functions. Also, we confirm their performance and estimate the energy consumption in simulated neural networks for simple neuromorphic applications, such as handwritten image recognition. Overall, our results highlight the potential of Pb-free materials to replace lead-based halide perovskites in memristors towards next-generation sustainable neuromorphic computing.

The funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Postdoctoral Fellowships (GA nº 101207139) is acknowledged.

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