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 paper develops reconfiguration approaches that enable post-production adaptation of circuit performance to enable RF IC re-use across different IOT applications. An adaptable low noise amplifier (LNA) is designed and fabricated in 130nm 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.
|Original language||English (US)|
|Journal||IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems|
|State||Accepted/In press - 2021|
- Adaptable RFIC
- Adaptive IoT Sensor
- Deep Learning.
- Internet of Things
- Low Noise Amplifier
- Machine Learning
- Performance evaluation
- Prediction algorithms
- Radio frequency
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering