Experimental Study of Low-Power CMOS Amplifiers
Jing Ivanov
The University of Manchester
Imran Karimi
National University of Sciences and Technology
Mohammad Al-Rashid
National University of Singapore
Abstract
Environmental pressures and resource constraints have brought low-power CMOS amplifiers to the forefront of research on manufacturing processes. Field surveys of manufacturing processes provided the dataset to which adaptive neuro-fuzzy inference was subsequently applied. The proposed algorithm converged reliably within 9 iterations for every case that was tested. The results support the adoption of the proposed method in routine engineering practice.
Keywords:
low-power CMOS amplifiersadaptive neuro-fuzzy inferencemaximum power point trackingarsenic removalresponse surface methodology
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