An example analog-test-bus (ATB) apparatus includes a plurality of comparator circuits, each having an output port, and a pair of input ports of opposing polarity including an inverting port and a non-inverting port, a plurality of circuit nodes to be selectively connected to the input ports of a first polarity, and at least one digital-to-analog converter (DAC) to drive the input ports of the plurality of comparator circuits. The apparatus further includes data storage and logic circuitry that accounts for inaccuracies attributable to the plurality of comparator circuits by providing, for each comparator circuit, a set of calibration data indicative of the inaccuracies for adjusting comparison operations performed by the plurality of comparator circuits.

A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

A digital output monitor circuit includes a first digital circuit that performs mutual conversion between serial data and parallel data, a second digital circuit that decodes data output from the first digital circuit and generates a control signal for an analog circuit, and a third digital circuit that converts at least the control signal for an analog circuit into digital data. The first digital circuit converts the data output from the third digital circuit into serial data and outputs as an output data signal.

A digital output monitor circuit includes a first digital circuit that performs mutual conversion between serial data and parallel data, a second digital circuit that decodes data output from the first digital circuit and generates a control signal for an analog circuit, and a third digital circuit that converts at least the control signal for an analog circuit into digital data. The first digital circuit converts the data output from the third digital circuit into serial data and outputs as an output data signal.

A fingerprint recognition method includes: acquiring a prestored number of historical defect pixels after a fingerprint recognition sensor captures a first fingerprint image; prohibiting performing matching recognition on the first fingerprint image upon the number of the historical defect pixels being greater than or equal to a first preset number threshold; detecting a number of damaged capturing modules of the fingerprint recognition sensor to obtain a number of current defect pixels; and updating the number of the historical defect pixels by using the number of the current defect pixels, the updated number of the historical defect pixels being used for determining whether to perform matching recognition on a fingerprint image captured next time.

A fingerprint recognition method includes: acquiring a prestored number of historical defect pixels after a fingerprint recognition sensor captures a first fingerprint image; prohibiting performing matching recognition on the first fingerprint image upon the number of the historical defect pixels being greater than or equal to a first preset number threshold; detecting a number of damaged capturing modules of the fingerprint recognition sensor to obtain a number of current defect pixels; and updating the number of the historical defect pixels by using the number of the current defect pixels, the updated number of the historical defect pixels being used for determining whether to perform matching recognition on a fingerprint image captured next time.

In some examples, systems and methods may be used to improve functional safety of analog or mixed-signal circuits, and, more specifically, to anomaly detection to help predict failures for mitigating catastrophic results of circuit failures. An example may include using a machine learning model trained to identify point anomalies, contextual or conditional anomalies, or collective anomalies in a set of time-series data collected from in-field detectors of the circuit. The machine learning models may be trained with data that has only normal data or has some anomalous data included in the data set. In an example, the data may include functional or design-for-feature (DFx) signal data received from an in-field detector on an analog component. A functional safety action may be triggered based on analysis of the functional or DFx signal data.