Analog circuits contribute to as much test cost as digital circuits. Traditional model-based testing (e.g. catastrophic, range-based, regression models) had limited success due to diversity of circuits and contributing metrics. We propose a model-free analog circuit testing using a combination of advanced signal processing and machine learning techniques with low computation and memory requirements. These unique features make the proposed test methodology scalable, reusable and suitable for both off-chip and on-chip test possibilities.
['Mehrdad Heydarzadeh', 'H. Luo', 'Mehrdad Nourani']
Model-Free Testing of Analog Circuits