Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Halide-perovskite materials have emerged as promising candidates for constructing reliable memristors, a key element for advancing neuromorphic computing systems. While several perovskite formulations have been tested, the nature of the external interfaces has not been exploited to its full potential. In this study, LiF is employed as an interfacial layer between a bromide-perovskite and the top contact. The interlayer acts as a source of Li + ions that facilitate the formation of conducting filaments, combining the high ionic conductivity of a halide perovskite and the small size of the Li + ion. The incorporation of a LiF layer significantly enhances device performance at low operation voltages (∼70–150 mV) with a gradual increase in conductance, rendering the devices suitable for analog computation. Overall, devices yield stable and highly reproducible results with high sensitivity to the external voltage. Notably, these devices demonstrate high cycling stability during >10 4 cycles with small variability in writing–erasing measurements. These findings underline the potential of LiF-enhanced memristors for reliable and energy-efficient neuromorphic computing applications. As a proof of concept, these low-voltage memristors successfully functioned as synaptic weights in an emulated deep neural network (DNN) for handwritten digit recognition. Importantly, the use of LiF as an interlayer should be universally valid for other families of materials used in memristor applications.
Source: ACS Applied Electronic Materials
Authors: Naresh Kumar Pendyala, Ignacio Sanjuán, Qun-Gao Chen, Wen-Ya Lee, Chu-Chen Chueh, Antonio Guerrero
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Awards: G280048558, G4616169043, G6231219559
OpenAlex ID: W7116040838