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This paper presents a low‐power self‐contained overvoltage detector. Compared with conventional detectors that consist of stacked Zener diodes, the proposed structure can trim the threshold with binary codes to accommodate system‐level changes. The proposed structure enables trimming by adding a decoder, a biasing circuit, and switches in parallel with Zener diodes. The newly configured structure forms a standalone detector that can operate independently and be powered from the supply under detection with negligible current addition. The proposed structure is applicable to any high‐voltage process that contains N‐type isolation layer and needs no high‐voltage device. The proposed circuit with three bits adjustment circuit was fabricated in a 0.18 μm Bipolar‐CMOS‐DMOS (BCD) process, the overvoltage threshold can be adjusted from 8 to 38 V with post trim variation of ±2.5 V. The measured quiescent current was around 0 μA at 10 V and 28 μA at 38 V, which was comparable to the current consumption in a conventional structure with 38 V overvoltage threshold. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Semisupervised classification aims to construct a classifier by making use of both labeled data and unlabeled data. This paper proposes a semisupervised classifier based on a piecewise linear regression model implemented by using a gated linear network. The semisupervised classifier is constructed in two steps. In the first step, instead of estimating the break points of a piecewise linear model directly, a label‐guided autoencoder‐based semisupervised gating mechanism is designed to generate binary gate control signals to realize the partitioning. In the second step, the piecewise linear model is first transformed into linear regression form, and the linear parameters are then optimized globally by a Laplacian regularized least squares (LapRLS) algorithm using a kernel function comprising the gate control signals obtained in the first step. Moreover, the composed kernel function is used as a better similarity function for the graph construction. As a result, we capture data manifold from both labeled and unlabeled data, and the data manifold is ingeniously incorporated into both the kernel and the graph Laplacian in LapRLS. Numerical experiments on various real‐world datasets exhibit the effectiveness of the proposed method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]