Sung Hyeon Park,Dong Gue Roe,Sang Young Jeong,Yoon Young Choi,Duho Jang,Sung Joon Cheon,Min Sub Kim,Yeong Don Park,Youngjae Yoo,Dong-Hwan Kim,Han Young Woo,Jeong Ho Cho
{"title":"Temporal Effect Analysis in an In-Sensor Computing System Enabled by Retention-Engineered Synaptic Devices.","authors":"Sung Hyeon Park,Dong Gue Roe,Sang Young Jeong,Yoon Young Choi,Duho Jang,Sung Joon Cheon,Min Sub Kim,Yeong Don Park,Youngjae Yoo,Dong-Hwan Kim,Han Young Woo,Jeong Ho Cho","doi":"10.1021/acssensors.5c01495","DOIUrl":null,"url":null,"abstract":"In-sensor computing systems demonstrate significant potential for reducing system complexity and enhancing computational efficiency. However, current methodologies predominantly focus on monitoring and processing instantaneous sensor data, neglecting the crucial temporal aspects of sensor inputs. This limitation is particularly significant in healthcare applications, where the human body often exhibits delayed responses to external stimuli. Herein, we developed a synapse-based in-sensor computing system that comprises three retention-engineered synaptic devices connected in parallel to represent the temporal effects of external stimuli. In a proof-of-concept application, the synapse-based in-sensor computing system accurately evaluated the combined temporal risk of hazardous gases, presenting a novel method for assessing the synergistic hazardousness of multiple gases. The presented retention-engineered synapse-based in-sensor computing system offers an innovative solution to the challenges of traditional in-sensor computing, providing a pathway to reduce system complexity and enhance computational efficiency.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"23 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Sensors","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acssensors.5c01495","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In-sensor computing systems demonstrate significant potential for reducing system complexity and enhancing computational efficiency. However, current methodologies predominantly focus on monitoring and processing instantaneous sensor data, neglecting the crucial temporal aspects of sensor inputs. This limitation is particularly significant in healthcare applications, where the human body often exhibits delayed responses to external stimuli. Herein, we developed a synapse-based in-sensor computing system that comprises three retention-engineered synaptic devices connected in parallel to represent the temporal effects of external stimuli. In a proof-of-concept application, the synapse-based in-sensor computing system accurately evaluated the combined temporal risk of hazardous gases, presenting a novel method for assessing the synergistic hazardousness of multiple gases. The presented retention-engineered synapse-based in-sensor computing system offers an innovative solution to the challenges of traditional in-sensor computing, providing a pathway to reduce system complexity and enhance computational efficiency.
期刊介绍:
ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.