Sahmyook University Researchers Open Doors to Next-Gen Memristive Devices

Memristive devices constitute a category of devices capable of retaining their internal resistance, thus offering superior performance compared to conventional devices that use integrated circuits.

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Several materials have been explored to manufacture these devices. In recent years, transition metal oxides have gradually become widely popular for this purpose.

Researchers have developed a silver-dispersive chalcogenide thin film as a resistance-switching material for memristive devices, which enables low-power operation and boosts device reliability and endurance.

Due to their increasing application in diverse domains like AI systems, memristive devices must now overcome several issues related to data retention, endurance, and a large number of conductance states. Moreover, the individual fabrication of these devices is time-consuming. As a result, several challenges need to be addressed to improve their performance and reliability.

In a recent study led by Professor Min Kyu Yang, Sahmyook University, Korea, researchers have developed a silver (Ag)-dispersive chalcogenide thin film for use as a resistance-switching material in memristive devices. Their paper was made available online on October 27, 2023, and published in Volume 664 of the journal Applied Surface Science on January 30, 2024.

The proposed thin film facilitates an ‘electro-forming-free’ process (it does not require an electric current to induce chemical change before manufacture or operation), allowing low-power operation via formation of an active layer.

Prof. Yang  said: “Our diffusive Ag-based memristive device in a chalcogenide thin film shows low power consumption and mimics the human brain’s parallel processing. This makes it suitable for implementation in crossbar arrays, and it achieved ~ 92% recognition rate in the MNIST (Modified National Institute of Standards and Technology) handwritten digit recognition database.“

In this study, the researchers utilized a multitude of spectroscopic techniques to characterize the film material, including high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, Auger electron spectroscopy, and Rutherford backscattering spectroscopy. The team also analyzed various electrodes and resistive switching layers to reveal the significant role played by the Ag atom.

The device demonstrated both state retention and reliable endurance even in a challenging environment of 85℃ for 2h. This study thus emphasizes the capability of chalcogenide materials to enhance the performance of memristive devices.

The present technology is expected to address the need to increase the memory capacity of semiconductors for big data applications, where the terabyte unit of storage is now considered inadequate. However, this poses the challenge of managing a large volume of chips. Consequently, the ‘neuromorphic chip’ is being developed as the next-gen semiconductor for AI systems. These chips need to possess characteristics like low power consumption, compact size, and the capability to analyze human behavior patterns.

“Employing the diffusive Ag-based memristive device structures could lead to the development of neuromorphic chips that could find extensive applications in the Fourth Industrial Revolution markets, including data analysis, speech recognition, facial recognition, autonomous vehicles, and the Internet of Things, as well as contributing to the ongoing 5G communication revolution,” envisions Yang.

Article : Ag-dispersive chalcogenide media for readily activated electronic memristor

Applied Surface Science has published an article written by Su Yeon Lee, Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul 01795, Republic of Korea, Jin Joo Ryu, Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea, Hyun Kyu Seo, Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul 01795, Republic of Korea, Hyunchul Sohn, Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea, Gun Hwan Kim, Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea, and Department of System Semiconductor Engineering, Yonsei University, Seoul 03722, Republic of Korea, and Min Kyu Yang, Intelligent Electronic Device Lab, Sahmyook University, 815 Hwarang-ro, Nowon-Gu, Seoul 01795, Republic of Korea.

Memristive device of the electro-forming free and initial ‘ON-state’ is introduced using a Ag-dispersed chalcogenide Ge₂Se₃Te₅ thin film. Unlike conducting-filament-based memristive devices, the Ag/GST/W-structured memristive device exhibits a narrow operating distribution and highly reliable performance. The microscopic origin of the desirable device performance is presented from the viewpoint of materials and device structure.

Abstract: “In this paper, we report a Ag-dispersive chalcogenide thin film as a resistance-switching material for memristive devices. The memristive device with Ag/Ge₂Se₃Te₅/W showed an initial low resistance state in its pristine stage (electro-forming-free), a low power consumption of 60 nW, robust state retention in a harsh environment of 85 °C for 2 h, and reliable endurance. We used high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, Auger electron spectroscopy, and electrical conduction characteristics to determine the microscopic origin of the memristive device. Comparative studies of different electrodes and resistive switching layers indicated that the diffusive Ag atom in the chalcogenide thin film plays a crucial role in realizing distinctive memristive characteristics. Further, we highlighted that the observed favorable performance of the memristive device is possible with a chalcogenide material, which can serve as an electrolyte for high ion diffusion and desirable electronic traps. Moreover, the memristive device exhibited an analogous conductance variation in a nonvolatile manner, which can be adopted as an artificial synaptic device. Based on the observed synaptic performance, an inference accuracy of ∼92% was achieved using handwritten numbers from the Modified National Institute of Standards and Technology database.“