An In-Field Programmable Adaptive CMOS LNA for Intelligent IoT Sensor Node Applications
As the Internet of Things (IoT) is growing rapidly, there is an emerging need to facilitate development of IoT devices in the design cycle while optimized performance is obtained in the field of operation. This article develops reconfiguration approaches that enable post-production adaptation of cir...
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| Veröffentlicht in: | IEEE transactions on computer-aided design of integrated circuits and systems 2022-02, Vol.41 (2), p.201-210 |
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| container_title | IEEE transactions on computer-aided design of integrated circuits and systems |
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| creator | Shafiee, Maryam Ozev, Sule |
| description | As the Internet of Things (IoT) is growing rapidly, there is an emerging need to facilitate development of IoT devices in the design cycle while optimized performance is obtained in the field of operation. This article develops reconfiguration approaches that enable post-production adaptation of circuit performance to enable RF IC reuse across different IoT applications. An adaptable low noise amplifier (LNA) is designed and fabricated in 130-nm CMOS technology to investigate the post-production reconfiguration concept. A statistical model that relates circuit-level reconfiguration parameters to circuit performances is generated by characterizing a limited number of samples. A deep learning algorithm is used to generate the model. This model is used to predict the performance parameters of the device in the field. The estimation error for LNA performance parameters are obtained in the simulation environment as well as chip measurements. |
| doi_str_mv | 10.1109/TCAD.2021.3077196 |
| format | Article |
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This article develops reconfiguration approaches that enable post-production adaptation of circuit performance to enable RF IC reuse across different IoT applications. An adaptable low noise amplifier (LNA) is designed and fabricated in 130-nm CMOS technology to investigate the post-production reconfiguration concept. A statistical model that relates circuit-level reconfiguration parameters to circuit performances is generated by characterizing a limited number of samples. A deep learning algorithm is used to generate the model. This model is used to predict the performance parameters of the device in the field. 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The estimation error for LNA performance parameters are obtained in the simulation environment as well as chip measurements.</description><subject>Adaptable RFIC</subject><subject>adaptive IoT sensor</subject><subject>Algorithms</subject><subject>Amplifier design</subject><subject>Calibration</subject><subject>CMOS</subject><subject>deep learning</subject><subject>Design optimization</subject><subject>Integrated circuits</subject><subject>Internet of Things</subject><subject>Low noise</subject><subject>low noise amplifier (LNS)</subject><subject>Machine learning</subject><subject>Parameters</subject><subject>Performance evaluation</subject><subject>Prediction algorithms</subject><subject>Radio frequency</subject><subject>Reconfiguration</subject><subject>Statistical methods</subject><subject>Statistical models</subject><subject>Switches</subject><subject>Tuning</subject><issn>0278-0070</issn><issn>1937-4151</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQQBdRsFZ_gHgJeE7d2Y9s9hii1UJthVY8LpvtbElJk7hJBf-9KS2eBob3ZuARcg90AkD10zrPnieMMphwqhTo5IKMQHMVC5BwSUaUqTSmVNFrctN1O0pBSKZH5Curo1kdT0usNtFHaLbB7ve2qDDKNrbtyx-M8vflKpovssg3YWB7rKpyi3UfzZp1tMK6G9aLZjMYbVuVzvZlU3e35MrbqsO78xyTz-nLOn-L58vXWZ7NY8eE7GPvmCycpV4J7Z2WClTikSMXSlDrKLfoCok2YVwXDEFyzcEnwFyKIk0lH5PH0902NN8H7Hqzaw6hHl4aljCgSSKHDGMCJ8qFpusCetOGcm_DrwFqjv3MsZ859jPnfoPzcHJKRPzntWAClOR_g_hqCA</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Shafiee, Maryam</creator><creator>Ozev, Sule</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on computer-aided design of integrated circuits and systems, 2022-02, Vol.41 (2), p.201-210 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptable RFIC adaptive IoT sensor Algorithms Amplifier design Calibration CMOS deep learning Design optimization Integrated circuits Internet of Things Low noise low noise amplifier (LNS) Machine learning Parameters Performance evaluation Prediction algorithms Radio frequency Reconfiguration Statistical methods Statistical models Switches Tuning |
| title | An In-Field Programmable Adaptive CMOS LNA for Intelligent IoT Sensor Node Applications |
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