Lingli Liu, Putu Andhita Dananjaya, Eng Kang Koh, Funan Tan, Ze Chen, Gerard Joseph Lim, Calvin Xiu Xian Lee, Jin-Lin Yang, Wen Siang Lew
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CMOS-Compatible Protonic Three-Terminal Memristor for Analog Synapse in Neuromorphic Computing.
All-solid-state inorganic hydrogen-based three-terminal memristors (H-3TMs) suffer from poor retention, susceptibility to humidity and temperature, and the reliance on wet chemistry during fabrication, hindering their manufacturability within existing foundry processes. To address these, this study presents a CMOS-compatible H-3TM based on reversible intercalation and extraction of protons between the SiNx electrolyte and WOx channel. The protons are introduced via a straightforward hydrogen plasma treatment, promoting a compatible fabrication process with back-end-of-line integration. Experimental and simulation results indicate that the low proton transport tendency across the electrolyte/channel interface without an external electric field contributes to high retention performance. Furthermore, the device demonstrates linear potentiation and depression, 512 conductance states with a dynamic range of ≈40, low energy operation (≈73 fJ per write), and excellent overall device-to-device variation. Its analog properties are evaluated under the training and inference framework of MNIST and Fashion-MNIST datasets. The device achieved training and inference accuracies only 0.4% and 0.3% below the ideal benchmark on the F-MNIST dataset. This work offers a rational approach for future artificial synaptic device design and fabrication.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.