Abstract
This paper develops a back-propagation neural network (BPNN) and a recurrent neural network (RNN) to predict long-term degradation of CMOS low noise amplifiers due to NBTI and HCI. It thus responds to the challenge of long experimental time caused by low stress voltages. A CMOS low noise amplifier (LNA) is designed and fabricated to test the models. The impacts of NBTI and HCI on the LNA are investigated by the Measure/Stress/Measure (MSM) technique. It is shown that the effects of NBTI and HCI on the LNA are frequency dependent and S-parameters of the LNA are sensitive to the circuit aging. The measured S21, S11, and S22 at 500 MHz are selected as the degradation indicators. The 6-step-ahead, 9-step-ahead, and 12-step-ahead predictions for the LNA aging are developed based on the neural networks. A comparative study of the predicted results obtained from the BPNN and RNN models is carried out to appraise the prediction capability of these models. The results show that the BPNN has a better performance for multi-step-ahead prediction of circuit aging.
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Acknowledgment
This work was supported in part by the National Key R&D Program of China under Grant 2016YFA0202200, and in part by the AoShan Talents Outstanding Scientist Program Supported by Pilot National Laboratory for Marine Science and Technology under Grant 2017ASTCP-OS03, and the Leading Talents of Guangdong Province Program under Grant 2016LJ06D557.
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Yang, C., Feng, F. Multi-Step-Ahead Prediction for a CMOS Low Noise Amplifier Aging Due to NBTI and HCI Using Neural Networks. J Electron Test 35, 797–808 (2019). https://doi.org/10.1007/s10836-019-05843-7
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DOI: https://doi.org/10.1007/s10836-019-05843-7