Influence of passive oscillator component variation on OBT sensitivity in OTAs

Petrashin, Pablo; Toledo, Luis; Lancioni, Wálter; Vázquez, Carlos Daniel; Stander, Tinus; Dualibe, Fortunato

doi:10.1109/latw.2018.8349689

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OBT and OBIST have been applied to a wide variety of analogue and mixed-signal (AMS) CMOS circuits, including operational transimpedance amplifiers (OTAs) [28], [29] and OTA-based filters [30], second-generation current conveyors (CCIIs) [31] and CCII-based filters [32], conventional op-amps [33], switched capacitor filters [34], [35], digital circuits such as digital filters [36], full AMS blocks [37] and phase shifters [38].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Nel

Dualibe

Stander

2023

IEEE Open J. Circuits Syst.

View full text Add to dashboard Cite

Oscillation-based testing (OBT) and Oscillation-based built-in self-testing (OBIST) circuits enable detection of catastrophic faults in analogue and RF circuits, but both are sensitive to process, voltage and temperature (PVT) variation. This paper investigates 15 OBT and OBIST feature extraction strategies, and four approaches to threshold selection, by calculating figure-of-merit (FoM) across PVT variation. This is done using a 2.4 GHz LNA in 0.35 μm CMOS as DUT. Of the 15 feature extraction approaches, the OBT approaches are found more effective, with some benefit gained from switched-state detection. Of the four approaches to threshold selection (nominal-ranged static thresholds, extreme-range static thresholds, temperature dynamic thresholds, and simple noise-filtered tone detection), dynamic thresholds resulted in the highest average FoM of 0.919, with the best FoM of 0.952, with a corresponding probability of test escape P(TE) and yield loss P(YL) of 5•10 -2 and 1.89•10 -2 respectively but requires accurate temperature measurement. Extreme static threshold selection resulted in a comparable average FoM of 0.912, but with less susceptibility to process variation and without the need for temperature measurement. Binary detection of a noisefiltered oscillating tone is found the least complex approach, with an average FoM of 0.891.

show abstract

Section: Introductionmentioning

confidence: 99%

“…OBT uses test equipment to measure the oscillation parameters [25], [27], and may also be used to establish the feedback loop [29]. At RF frequencies, these instruments typically only measure scalar values of frequency and power [38], but at least one RF node needs to be accessed off-chip in the case of RF OBT CMOS testing.…”

Section: Introductionmentioning

confidence: 99%

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Nel

Dualibe

Stander

2023

IEEE Open J. Circuits Syst.

View full text Add to dashboard Cite

show abstract

Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches

2022

Self Cite

View full text Add to dashboard Cite

Oscillation-based testing of analogue electronic filters removes the need for test signal synthesis. Parametric faults in the presence of normal component tolerance variation are challenging to detect and diagnose. This study demonstrates the suitability of statistical learning and deep learning techniques for parametric fault diagnosis and detection by investigating several time-series classification techniques. Traditional harmonic analysis is used as a baseline for an in-depth comparison. Eight standard classification techniques are applied and compared. Deep learning approaches, which classify the timeseries signals directly, are shown to benefit from the oscillator startup region for feature extraction. Global average pooling in the convolutional neural networks (CNN) allows for Class Activation Maps (CAM).This enables interpreting the time-series signal's discriminative regions and confirming the importance of the start-up oscillation signal. The deep learning approach outperforms the harmonic analysis approach on simulated data by an average of 11.77% in classification accuracy for the three parametric fault magnitudes considered in this work.

show abstract

Influence of Oscillator Topology on Fault Sensitivity in Oscillation Based Testing (OBT) of OTAs

Stander

Petrashin

Toledo

et al. 2019

2019 Argentine Conference on Electronics (CAE)

View full text Add to dashboard Cite

Influence of passive oscillator component variation on OBT sensitivity in OTAs

Cited by 4 publications

References 8 publications

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches

Influence of Oscillator Topology on Fault Sensitivity in Oscillation Based Testing (OBT) of OTAs

Contact Info

Product

Resources

About