Publication date: 24th July 2026
Memristive devices have emerged as promising candidates for next-generation computing systems, offering opportunities to overcome the limitations of conventional von Neumann architectures through low-power operation, analog information processing, and inherent memory functionalities. Among the various material platforms investigated, solution-processed metal oxide memristors out due to their compatibility with low-cost fabrication, scalability, and environmentally conscious manufacturing approaches.
This talk will present recent advances in solution-based memristive technologies, focusing on amorphous metal oxides such as zinc oxide (ZnO), zinc–tin oxide (ZTO), indium gallium zinc oxide (IGZO), aluminum oxide (AlOx), and molybdenum oxide (MoOx) and 2D materials. The influence of precursor chemistry, processing parameters, and device engineering on resistive switching performance will be discussed, highlighting strategies to achieve improved stability, reproducibility, and tunable electrical characteristics.
Recent developments in sustainable and printed memristors will be showcased, demonstrating their potential for flexible and eco-designed electronic systems. Beyond memory applications, the talk will explore the implementation of solution-processed memristors in neuromorphic computing, including synaptic functionalities, short-term plasticity, multilevel conductance states, and physical reservoir computing. These results underline the potential of solution-derived memristive devices as energy-efficient hardware platforms for edge artificial intelligence, and distributed sensing systems.
Finally, perspectives on the integration of sustainable materials, printed electronics, and neuromorphic architectures will be discussed, outlining future directions toward scalable, low-cost, and environmentally friendly computing technologies.
This work received funding from the HORIZON-EIC-2023-PATHFINDERCHALLENGES-01 program, grant agreement no. 101161114 (ELEGANCE). This project was also partially suported by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101256099 (GAIA).
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