2023/06/22 by Ertürk Enver Yıldırım, Luis-Antonio Panes-Ruiz, Yildirim, Ertürk Enver +9
Engineering · Materials Science · Neuroscience · #Adaptation and Self-Organizing Systems (nlin.AO) #Advanced Memory and Neural Computing #B.3.1 Semiconductor Memories #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Photoreceptor and optogenetics research #Transition Metal Oxide Nanomaterials
paper · pdf · doi:10.48550/arxiv.2306.12720
openalex publication_date 2023/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, neuromorphic computing has gained attention as a promising approach to enhance computing efficiency. Among existing approaches, neurotransistors have emerged as a particularly promising option as they accurately represent neuron structure, integrating the plasticity of synapses along with that of the neuronal membrane. An ambipolar character could offer designers more flexibility in customizing the charge flow to construct circuits of higher complexity. We propose a novel design for an ambipolar neuromorphic transistor, utilizing carbon nanotubes as the semiconducting channel and an ion-doped sol-gel as the polarizable gate dielectric. Due to its tunability and high dielectric constant, the sol-gel effectively modulates the conductivity of nanotubes, leading to efficient and controllable short-term potentiation and depression. Experimental results indicate that the proposed design achieves reliable and tunable synaptic responses with low power consumption. Our findings suggest that the method can potentially provide an efficient solution for realizing more adaptable cognitive computing systems.