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Halide perovskite-based memory devices and neuromorphic computing.

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Title: Halide perovskite-based memory devices and neuromorphic computing.
Authors: Paul, Tufan1 (AUTHOR), Sahoo, Aditi2 (AUTHOR), Maiti, Soumen3 (AUTHOR), Pal, Pulak4 (AUTHOR), Chattopadhyay, Kalyan Kumar1,4 (AUTHOR) kalyan_chattopadhyay@yahoo.com
Source: Dalton Transactions: An International Journal of Inorganic Chemistry. 5/12/2026, Vol. 55 Issue 18, p7019-7035. 17p.
Subjects: Artificial synapses, Nonvolatile random-access memory, Computer storage devices, Perovskite analysis, Internet of things, Information storage & retrieval systems, Artificial neural networks
Abstract: The swift rise of digital communications, particularly in the realm of big data and the Internet of Things (IoT), has accelerated the development of next-generation data storage technologies. Resistive switching (RS) memory devices and artificial synapses remain appealing alternatives, offering low power consumption, ability to accommodate various capacities, and rapid speed. Neuromorphic computing aims to simulate the neuronal structure and functioning of the human brain, enabling advancements in human perception, interpretation, and autonomous adaptation. Halide perovskites are a group of materials that possess several benefits, including a significant distance over which charge carriers can move, a strong ability to absorb light, the ability to carry both positive and negative charges, the ability to conduct ions, and the ability to be processed in solution. Photovoltaics, light-emitting diodes, lasers, and photodetectors are merely some of the numerous applications in which they have proven useful. This article provides an extensive review of the most contemporary advancements in halide perovskite-based artificial synapses and RS memory devices. To begin with, this paper introduces the overall structure and distinctive features of RS memory devices. Next, we delve into the exceptional memory performance supported by comprehensive operational processes. This review also aims at laying the groundwork for the rational development of halide perovskite memory devices and artificial synapses, which will lead to notable performance improvements. Lastly, current obstacles and the possibilities for future progress are discussed. [ABSTRACT FROM AUTHOR]
Copyright of Dalton Transactions: An International Journal of Inorganic Chemistry is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: The swift rise of digital communications, particularly in the realm of big data and the Internet of Things (IoT), has accelerated the development of next-generation data storage technologies. Resistive switching (RS) memory devices and artificial synapses remain appealing alternatives, offering low power consumption, ability to accommodate various capacities, and rapid speed. Neuromorphic computing aims to simulate the neuronal structure and functioning of the human brain, enabling advancements in human perception, interpretation, and autonomous adaptation. Halide perovskites are a group of materials that possess several benefits, including a significant distance over which charge carriers can move, a strong ability to absorb light, the ability to carry both positive and negative charges, the ability to conduct ions, and the ability to be processed in solution. Photovoltaics, light-emitting diodes, lasers, and photodetectors are merely some of the numerous applications in which they have proven useful. This article provides an extensive review of the most contemporary advancements in halide perovskite-based artificial synapses and RS memory devices. To begin with, this paper introduces the overall structure and distinctive features of RS memory devices. Next, we delve into the exceptional memory performance supported by comprehensive operational processes. This review also aims at laying the groundwork for the rational development of halide perovskite memory devices and artificial synapses, which will lead to notable performance improvements. Lastly, current obstacles and the possibilities for future progress are discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Dalton Transactions: An International Journal of Inorganic Chemistry is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1039/d5dt02579f
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              Text: 5/12/2026
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