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.
The huge amount of data generated by the current society makes it necessary to explore new computing methods with higher efficiency to overcome the bottleneck formed between data storage and processing tasks. Neuromorphic computing aims at emulating the functioning of our brain, which performs both tasks utilizing the same hardware. Here, we propose ambipolar field-effect transistors based on carbon nanotubes with a polarizable gate dielectric, capable of providing memory functions reminiscent of neuronal synapses, at both polarities of the device. The ambipolar characteristic doubles the possibilities of previously demonstrated neurotransistors. The short-term and ambipolar behavior of the device can find its place in novel applications in the future. Machine learning-enabled gas sensing is an excellent example, where real-time processing of large amounts of data is beneficial. In addition, interaction with oxidative and reductive gases will result in dual responses due to the ambipolarity of the transistor, along with the possibility of storing the sensing data.
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.
The huge amount of data generated by the current society makes it necessary to explore new computing methods with higher efficiency to overcome the bottleneck formed between data storage and processing tasks. Neuromorphic computing aims at emulating the functioning of our brain, which performs both tasks utilizing the same hardware. Here, we propose ambipolar field-effect transistors based on carbon nanotubes with a polarizable gate dielectric, capable of providing memory functions reminiscent of neuronal synapses, at both polarities of the device. The ambipolar characteristic doubles the possibilities of previously demonstrated neurotransistors. The short-term and ambipolar behavior of the device can find its place in novel applications in the future. Machine learning-enabled gas sensing is an excellent example, where real-time processing of large amounts of data is beneficial. In addition, interaction with oxidative and reductive gases will result in dual responses due to the ambipolarity of the transistor, along with the possibility of storing the sensing data.
Carbon nanotube neurotransistors with ambipolar memory and learning functions
