Osamah Alharbi , Sebastian Pazos , Kaichen Zhu , Fernando Aguirre , Yue Yuan , Xinyi Li , Huaqiang Wu , Mario Lanza
{"title":"在硅微型芯片中集成银基阈值开关器件","authors":"Osamah Alharbi , Sebastian Pazos , Kaichen Zhu , Fernando Aguirre , Yue Yuan , Xinyi Li , Huaqiang Wu , Mario Lanza","doi":"10.1016/j.mser.2024.100837","DOIUrl":null,"url":null,"abstract":"<div><p>Threshold-type resistive switching (RS) is an essential electronic behavior in many types of integrated circuits and can be exploited in multiple applications, such as leaky integrate-and-fire neurons for artificial neural networks (ANNs). Many research articles have shown that metal/insulator/metal (MIM) devices using Ag electrodes exhibit stable threshold-type RS, but all of them presented large devices (>1 µm<sup>2</sup>) fabricated on unfunctional SiO<sub>2</sub>/Si substrates. In this article, for the first time we integrate Ag-based threshold-type RS devices at the back-end-of-line interconnections of silicon microchips. The insulator used is multilayer hexagonal boron nitride (h-BN), and the size of the devices is ∼0.05 µm<sup>2</sup>. The devices can switch between a high resistive state (HRS) and a low resistive state (LRS) without the need of any forming process, and we observe a high endurance over 1 million cycles over multiple devices. By performing circuit simulation using SPICE software, we confirm that this electrical behavior is suitable for being used as leaky integrate-and-fire electronic neuron in spiking neural networks for image recognition, and the h-BN artificial neurons operate correctly for 94 % of the images presented. Our study represents a significant advancement towards the integration of Ag-based threshold-type RS devices in silicon microchips.</p></div>","PeriodicalId":386,"journal":{"name":"Materials Science and Engineering: R: Reports","volume":"161 ","pages":"Article 100837"},"PeriodicalIF":31.6000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integration of Ag-based threshold switching devices in silicon microchips\",\"authors\":\"Osamah Alharbi , Sebastian Pazos , Kaichen Zhu , Fernando Aguirre , Yue Yuan , Xinyi Li , Huaqiang Wu , Mario Lanza\",\"doi\":\"10.1016/j.mser.2024.100837\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Threshold-type resistive switching (RS) is an essential electronic behavior in many types of integrated circuits and can be exploited in multiple applications, such as leaky integrate-and-fire neurons for artificial neural networks (ANNs). Many research articles have shown that metal/insulator/metal (MIM) devices using Ag electrodes exhibit stable threshold-type RS, but all of them presented large devices (>1 µm<sup>2</sup>) fabricated on unfunctional SiO<sub>2</sub>/Si substrates. In this article, for the first time we integrate Ag-based threshold-type RS devices at the back-end-of-line interconnections of silicon microchips. The insulator used is multilayer hexagonal boron nitride (h-BN), and the size of the devices is ∼0.05 µm<sup>2</sup>. The devices can switch between a high resistive state (HRS) and a low resistive state (LRS) without the need of any forming process, and we observe a high endurance over 1 million cycles over multiple devices. By performing circuit simulation using SPICE software, we confirm that this electrical behavior is suitable for being used as leaky integrate-and-fire electronic neuron in spiking neural networks for image recognition, and the h-BN artificial neurons operate correctly for 94 % of the images presented. Our study represents a significant advancement towards the integration of Ag-based threshold-type RS devices in silicon microchips.</p></div>\",\"PeriodicalId\":386,\"journal\":{\"name\":\"Materials Science and Engineering: R: Reports\",\"volume\":\"161 \",\"pages\":\"Article 100837\"},\"PeriodicalIF\":31.6000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Science and Engineering: R: Reports\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0927796X24000676\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Science and Engineering: R: Reports","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927796X24000676","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Integration of Ag-based threshold switching devices in silicon microchips
Threshold-type resistive switching (RS) is an essential electronic behavior in many types of integrated circuits and can be exploited in multiple applications, such as leaky integrate-and-fire neurons for artificial neural networks (ANNs). Many research articles have shown that metal/insulator/metal (MIM) devices using Ag electrodes exhibit stable threshold-type RS, but all of them presented large devices (>1 µm2) fabricated on unfunctional SiO2/Si substrates. In this article, for the first time we integrate Ag-based threshold-type RS devices at the back-end-of-line interconnections of silicon microchips. The insulator used is multilayer hexagonal boron nitride (h-BN), and the size of the devices is ∼0.05 µm2. The devices can switch between a high resistive state (HRS) and a low resistive state (LRS) without the need of any forming process, and we observe a high endurance over 1 million cycles over multiple devices. By performing circuit simulation using SPICE software, we confirm that this electrical behavior is suitable for being used as leaky integrate-and-fire electronic neuron in spiking neural networks for image recognition, and the h-BN artificial neurons operate correctly for 94 % of the images presented. Our study represents a significant advancement towards the integration of Ag-based threshold-type RS devices in silicon microchips.
期刊介绍:
Materials Science & Engineering R: Reports is a journal that covers a wide range of topics in the field of materials science and engineering. It publishes both experimental and theoretical research papers, providing background information and critical assessments on various topics. The journal aims to publish high-quality and novel research papers and reviews.
The subject areas covered by the journal include Materials Science (General), Electronic Materials, Optical Materials, and Magnetic Materials. In addition to regular issues, the journal also publishes special issues on key themes in the field of materials science, including Energy Materials, Materials for Health, Materials Discovery, Innovation for High Value Manufacturing, and Sustainable Materials development.