Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices

Abstract

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/HfO2/TaOx/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 HfO2 layer. Moreover, Pt/HfO2/TaOx/TaN with a 3 nm thick HfO2 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/HfO2/TaOx/TaN with Pt/TaOx/TaN, which has insufficient performance to be used as a nociceptor.

An Artificial LiSiOx Nociceptor with Neural Blockade and Self-Protection Abilities

“…As a bionic device, artificial nociceptors can simulate the process of pain generation and have a wide application potential in areas such as electronic skins and artificial muscles, − bringing the gospel to individuals who have lost the ability to detect external stimuli of injury in real time. , Recently, a memristor-based artificial nociceptor was proposed to mimic the basic functions of a humanlike nociceptor and it provided a new approach for the simulation of artificial nociceptors due to their simple structure, stable resistive switching behavior, and low power consumption. ,− Current studies had mimicked pain perception, − i.e., threshold, inadaptation, relaxation, and sensitization by using typical metal-oxide (e.g., TiO 2 or HfO 2 ) memristors. ,,− However, research on the simulation of nociceptive blockade under continuous intense stimulation had been rarely reported, which was attributed to the special conductive mechanism issues of the functional layer. Among several oxide compounds, LiSiO x with a high ionic conductivity, thermal stability, and better biocompatibility is thus widely used in the research of neuromorphic devices. − LiSiO x was considered a highly feasible material for nociceptors with four basic functions due to its excellent resistance behavior.…”

Artificial Synapse Emulated by Indium Tin Oxide/SiN/TaN Resistive Switching Device for Neuromorphic System

“…Additionally, charge-based NAND flash memory is about to face limitations in nonvolatile storage technology due to its scaling limits [5]. To address the aforementioned issues, different next-generation non-volatile memories such as phase-change memory (PCM) [6], magnetic random-access memory (MRAM) [7], ferroelectric random-access memory (FRAM) [8], and resistive random-access memory (RRAM) [2,[9][10][11][12][13] are emerging. RRAM is a viable contender among them due to its high scalability, low-power operation, fast switching speed, long retention time, and high endurance [14][15][16][17][18][19][20][21].…”

Double-Forming Mechanism of TaOx-Based Resistive Memory Device and Its Synaptic Applications

“…Furthermore, applying different biases with different polarities causes a phenomenon termed the electro-resistance effect, where the resistance condition changes between a low-resistance state (LRS) and a high-resistance state (HRS), and which information is stored at 0 s and 1 s, respectively [ 24 , 25 ]. Various transition metal oxides have been employed as resistive switching insulators, including HfO 2 [ 26 ], TiO 2 [ 27 , 28 , 29 ], TaO x [ 30 ], Al 2 O 3 [ 31 , 32 ], and ZnO [ 30 ]. Extensive research has been conducted on TaO x , revealing it as a promising candidate for the resistive switching layer [ 33 ].…”

“…Consequently, the filament ruptures and the device returns to HRS. The RRAM device stores memory in these two states, HRS and LRS, which can be consequently reproduced by applying sufficient bias [ 30 ]. To increase storage density, research has indicated that by applying multilevel cell (MLC) characteristics, a high storage density could be achieved due to multiple stable states between HRS and LRS.…”