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Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO 2 /TaOx/TaN Devices.

Minsu ParkBeomki JeonJongmin ParkSungjun Kim
Published in: Nanomaterials (Basel, Switzerland) (2022)
As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit is required to electrically realize a nociceptor, a memristor with threshold switching characteristics can implement the nociceptor as a single device. Here, we suggest a memristor with a Pt/HfO 2 /TaO x /TaN bilayer structure. This device can mimic the characteristics of a nociceptor including the threshold, relaxation, allodynia, and hyperalgesia. Additionally, we contrast different electrical properties according to the thickness of the HfO 2 layer. Moreover, Pt/HfO 2 /TaO x /TaN with a 3 nm thick HfO 2 layer has a stable endurance of 1000 cycles and controllable threshold switching characteristics. Finally, this study emphasizes the importance of the material selection and fabrication method in the memristor by comparing Pt/HfO 2 /TaO x /TaN with Pt/TaO x /TaN, which has insufficient performance to be used as a nociceptor.
Keyphrases
  • artificial intelligence
  • machine learning
  • neuropathic pain
  • deep learning
  • spinal cord
  • skeletal muscle
  • spinal cord injury
  • low cost
  • tissue engineering