CNN-based Stochastic Regression for IDDQ Outlier Identification

Chen, Chun-Teng; Yen, Chia-Heng; Wen, Cheng-Hao; Yang, Chenghao; Wu, Kai–Chiang; Chern, Mason; Chen, Ying-Yen; Kuo, Chin-Hwa; Lee, Jih-Nung; Kao, Shu-Yi; Chao, Mango C.-T.

doi:10.1109/vts48691.2020.9107570

Cited by 9 publications

(1 citation statement)

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“…Statistical analysis includes outlier detection, measurement data smoothing, and golden device selection. To detect outliers, researchers applied the principal component analysis [5], a Mahalanobis distance [6], and a convolutional neural network [7]. MMIC models are extracted from the golden device's measurements.…”

Section: Introductionmentioning

confidence: 99%

Automatic golden device selection and measurement smoothing algorithms for microwave transistor small-signal noise modeling

Salnikov

Добуш²,

Popov³

et al. 2022

Int. J. Microw. Wireless Technol.

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We propose the techniques for automatic processing of measurement results in the context of golden (typical) device selection and noise figure measurement. These techniques are for golden (typical) device selection and noise figure measurement processing. Automation of measurement result processing and microwave element modeling speeds up a modeling routine and decreases the risk of possible errors. The techniques are validated through modeling of 0.15 μm GaAs pHEMTs with 4 × 40 μm and 4 × 75 μm total gate widths. Two test amplifiers were designed using the developed models. The amplifier modeling results agree well with measurements which confirms the validity of the proposed techniques. The proposed algorithm is potentially applicable to other circuit types (switches, digital, power amplifiers, mixers, oscillators, etc.) but may require different settings in those cases. However, in the presented work, we validated the algorithm for the linear and low-noise amplifiers only.

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Section: Introductionmentioning

confidence: 99%