Adaptive biological systems dominate nature due to their flexibility. In contrast, human-fabricated electronic devices have limited flexibility and adaptability, as their functionality is mostly fixed during fabrication. Therefore, developing nature-inspired, stimuli-responsive devices has been a long-standing goal in research, promising advancements in technology. Such reconfigurable devices could revolutionize fields requiring self-adjustment, like neuromorphic computing based on neuron-like synaptic transistors for third-generation AI.
Adaptive devices use materials that change properties in response to external stimuli and remain stable under ambient conditions (humidity, temperature, light irradiation, etc.). In nature, biological systems change the electrical properties of cells like neurons, muscle cells, or touch receptors by pumping ions through the plasma membrane (ion channels). This situation is partially analogous to rechargeable batteries, which can reversibly move ions from one electrode to the other through the electrolyte when a certain voltage is applied. Indeed, the intercalation of ions in battery electrodes, typically made of Mixed Ionic-Electronic Conductors (MIEC), modifies the fundamental properties of these materials. Among mobile ions used for ion-modulation strategies, the widely studied Li+ and H+ are common examples.
In our recent papers in Applied Physics Review and Advanced Materials, we have used mobile oxide ions to implement a unique modulation strategy, dynamically tuning the oxygen content in perovskite oxides. We have demonstrated continuous and analog tuning of the oxygen content in ferrites, which strongly impacts multiple properties, including magnetic, optical, and electronic behavior [1]. Using this fundamental knowledge, we have designed and fabricated a disruptive, ultra-low energy consumption neuromorphic transistor with non-volatile multiple states for application in advanced computing [2].
References:
[1] Nizet P et al. Sort J & Tarancón A 2024, ‘Analog control of La0.5Sr0.5FeO3-δ electrical properties through oxygen deficiency induced magnetic transition‘, Appl. Phys. Rev. 11, 041426
[2] Langner P, Chiabrera F, Alayo N, Nizet P, Morrone L, Bozal-Ginesta C, Morata A & Tarancón A 2024, ‘Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing‘, Advanced materials, 2415743
