Intermediate Layer‐Assisted Trap Density Reduction in Low‐Power Optoelectronic Memristors for Multifunctional Systems

Abstract The rapid expansion of the Internet of Things demands low‐power devices that integrate memory, sensors, and logic functions. Perovskite materials show promise for low‐power optoelectronic memristors; however, challenges such as nonuniform trap distribution and uncontrolled filament formation hinder their resistive switching performance. To overcome these issues, a TiO 2 nanofilm via atomic layer deposition as a base layer for filament formation, is introduced. This layer passivates interfacial defects by forming strong chemical interactions with Pb 2+ and I − ions at the perovskite interface, significantly reducing trap densities (interface trap density decreases 15‐fold to 3.0 × 10 16 cm −3 , and bulk trap density to 1.8 × 10 14 cm −3 ). Improved energy band alignment enables efficient electron transport, yielding a low‐ V SET (+0.24 V) and excellent low‐power (≈0.7 µW) nonvolatile memory performance. Additionally, the device reliably detects near‐infrared illumination as an optical input and enables reconfigurable image recognition using a 5 × 5 array under combined stimuli. It also facilitates the implementation of complex logic gates, such as AND, OR, and flip‐flops. This paper demonstrates the potential for integrating nonvolatile memory, sensing, and logic functionalities into a single low‐power device through the incorporation of a TiO 2 nanolayer.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-02-03
DOI
https://doi.org/10.1002/adfm.202421080
Citations
12
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
8.77

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article

Intermediate Layer‐Assisted Trap Density Reduction in Low‐Power Optoelectronic Memristors for Multifunctional Systems

Jiwoong Yang, Muhammad Asghar Khan, Young Kyun Choi, Tae Hyuk Kim et al.
12 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
8.77
article

Intermediate Layer‐Assisted Trap Density Reduction in Low‐Power Optoelectronic Memristors for Multifunctional Systems

Jiwoong Yang, Muhammad Asghar Khan, Young Kyun Choi, Tae Hyuk Kim, Sang Heon Lee, Soong Ju Oh, Seunghyun Oh, Gyeong Min Lee, Soyeon Lee, Jae Won Shim, Min Jong Lee, Hyungju Ahn
article en
12 citations

Abstract

Abstract The rapid expansion of the Internet of Things demands low‐power devices that integrate memory, sensors, and logic functions. Perovskite materials show promise for low‐power optoelectronic memristors; however, challenges such as nonuniform trap distribution and uncontrolled filament formation hinder their resistive switching performance. To overcome these issues, a TiO 2 nanofilm via atomic layer deposition as a base layer for filament formation, is introduced. This layer passivates interfacial defects by forming strong chemical interactions with Pb 2+ and I − ions at the perovskite interface, significantly reducing trap densities (interface trap density decreases 15‐fold to 3.0 × 10 16 cm −3 , and bulk trap density to 1.8 × 10 14 cm −3 ). Improved energy band alignment enables efficient electron transport, yielding a low‐ V SET (+0.24 V) and excellent low‐power (≈0.7 µW) nonvolatile memory performance. Additionally, the device reliably detects near‐infrared illumination as an optical input and enables reconfigurable image recognition using a 5 × 5 array under combined stimuli. It also facilitates the implementation of complex logic gates, such as AND, OR, and flip‐flops. This paper demonstrates the potential for integrating nonvolatile memory, sensing, and logic functionalities into a single low‐power device through the incorporation of a TiO 2 nanolayer.

Advanced Functional MaterialsVol. 35(22)
Daegu Gyeongbuk Institute of Science and Technology (KR), Korea University (KR), Pohang TechnoPark (South Korea) (KR), Pohang Accelerator Laboratory
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Samsung
Openalex Percentile: Top 2%
Advanced Memory and Neural Computing
8.77
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