Publication date: 21st July 2025
High-performance halide-based perovskite memory devices have been developed exhibiting a variety of synaptic [1-4] and neuronal functions based on non-volatile, and volatile or threshold switching, memristors, respectively. [5] However, a key ingredient in these perovskite-based systems is the presence of the highly toxic lead, which hinders their further development and commercial use. A lead-free perovskite approach for memristive applications could enable sustainable devices opening the path for practical applications, despite the current performance gap compared to lead-based systems. Herein, we present our recent data on the fabrication and characterization of printable non-volatile and volatile memristors based on Lead-Free Perovskites for artificial synapses and neurons emulation, respectively. Our approach is based on solution-processed manufacturing using all-inorganic, sustainable perovskites (Bismuth based) compounds. Depending on the metal contact type being either silver or gold, devices exhibit either non-volatile or volatile memristive switching. The non-volatile memristors exhibit an ON/OFF ratio of >104 while demonstrating very good retention and cycling endurance characteristics exceeding 1000 seconds and 1000 cycles, respectively. Typical volatile devices exhibit an ON/OFF ratio of > 103 and require a low switching voltages of few volts. Furthermore, linear long term potentiation protocols accompanied with an abrupt resistance suppression under depression protocols are demonstrated being also tunable by light illumination. The on-demand selection of the operation mode by tuning the metallic contact type, offers a unique materials system based on lead-free perovskites opening the path for implementing artificial synapses and neurons emulation in a single chip.
The research project is implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 14728). The work has been supported by the European Union’s Horizon 2020 research and innovation program under project EMERGE (grant agreement no. 101008701), project INFRACHIP (grant agreement no. 101131822), and the HORIZON-EIC-2023-PATHFINDERCHALLENGES-01 call under Grant Agreement No. 101161114.
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