A method of identifying defects in an electronic assembly, comprising, by a processing unit, obtaining a grid of nodes representative of a location of electronic units of an electronic assembly, wherein each node is neighboured by at most eight other nodes, wherein a first plurality of nodes represents failed electronic units according to at least one test criterion, and a second plurality of nodes represents passing electronic units according to the least one first test criterion, based on the grid, determining at least one first and second straight lines, and attempting to connect the first and second straight lines into a new line, wherein if at least one node from the new line belongs to the second plurality of nodes, concluding that an electronic unit represented by the node on the grid is a failed electronic unit, thereby facilitating identification of a failed electronic unit on the substrate.

A method for analyzing device test data includes accessing a core analytics rule that is based on manufacturing data of a plurality of devices. Each of the plurality of devices are produced in one of a plurality of manufacturing facilities and are of a same type as a first device being tested on a tester. The method also includes receiving initial test results of a plurality of other devices of a same type tested at a testing facility, generating, based on the initial test results, an edge analytics rule, modifying the core analytics rule based on the edge analytics rule, wherein the modified core analytics rule including modified binning limits, applying the modified core analytics rule to testing data obtained by testing the first device, and determining, based on applying the modified core analytics rule, that the first device is an outlier with respect to the modified binning limits.

The present invention relates to a system and method for calculating measurement uncertainty and determining measurement decision risk. Measurement uncertainty is calculated based on a plurality of error contributors. Measurement decision risk is evaluated using the measurement uncertainty, and mitigation strategies are applied to lower the probability of false acceptance and the probability of false rejection.

A system chip, and a built-in self-test circuit and a self-test method thereof are provided. The system chip includes an analog front end circuit, a digital physical layer circuit and a built-in self-test circuit. The digital physical layer circuit is coupled to the analog front end circuit, and the built-in self-test circuit is coupled to the digital physical layer circuit and is arranged to test the analog front end circuit with aid of the digital physical layer circuit.

A technique for fine-granularity speed binning for a processing device is provided. The processing device includes a plurality of clock domains, each of which may be clocked with independent clock signals. The clock frequency at which a particular clock domain may operate is determined based on the longest propagation delay between clocked elements in that particular clock domain. The processing device includes measurement circuits for each clock domain that measure such propagation delay. The measurement circuits are replica propagation delay paths of actual circuit elements within each particular clock domain. A speed bin for each clock domain is determined based on the propagation delay measured for the measurement circuits for a particular clock domain. Specifically, a speed bin is chosen that is associated with the fastest clock speed whose clock period is longer than the slowest propagation delay measured for the measurement circuit for the clock domain.

System and method for autonomous trouble shooting of a unit under test (UUT) having a plurality of replaceable components include: a test station that stores an artificial intelligence (AI) program and a knowledge database (KDB) including acceptable test results for each test point represented by an acceptable test vector, a test probe to test the circuit card assembly; and an operator station to send commands to the test station via the communication network to teach the AI program to capture and store the acceptable test result for each test point of the UUT by the test probe, in the KDB, wherein the AI program commands the test probe to test the UUT, stores the test results in a test result vector, compares the test result vector with the stored acceptable test vector, and displays recommendation as which replaceable component in the UUT to be repaired or replaced.

A computerized method for IC classification, outlier detection and/or anomaly detection comprising using at least one hardware processor for testing each of the plurality of ICs in accordance with an IC design on a wafer, wherein the IC design comprises a plurality of sensors. The at least one hardware processor is used for testing each of the plurality of ICs by: collecting a plurality of sensor values, the plurality of sensor values including sensor values from each of the plurality of sensors; comparing the plurality of sensor values to a classification scheme, thereby obtaining a classification for each tested IC; and recording the classification of the tested IC.

An exemplary method of detecting a Trojan circuit in an integrated circuit is related to applying a test pattern comprising an initial test pattern followed by a corresponding succeeding test pattern to a golden design of the integrated circuit, wherein a change in the test pattern increases side-channel sensitivity; measuring a side-channel parameter in the golden design of the integrated circuit after application of the test pattern; applying the test pattern to a design of the integrated circuit under test; measuring the side-channel parameter in the design of the integrated circuit under test after application of the test pattern; and determining a Trojan circuit to be present in the integrated circuit under test when the measured side-channel parameters vary by a threshold.

A measurement device with automatic optimization capabilities comprises at least one signal processing component with a physical detector and a virtual detector component comprising at least one virtual detector for a signal processing component without physical detector. The physical detector is configured to physically measure a measurement value assigned to the signal processing component. The virtual detector component is configured to use a model of a signal processing chain from the physical detector to the location of the virtual detector. The model comprises at least one model parameter for the signal processing chain. The measurement device is configured to adapt the virtual detector component with respect to a measurement type for the signal to be measured. The virtual detector component is configured to use the model and the at least one measurement value. The virtual detector component is configured to determine a virtually determined value based on the model and the at least one measurement value. The measurement device is configured to use the virtually determined value to determine a setting for the measurement device. In addition, a method of setting a measurement device is described.

A method and a circuit for adaptive regulation of body bias voltages controlling nmos and pmos transistors of an integrated circuit includes a digital circuit, a counter, a control unit and a charge pump. A first ring oscillator monitor measures a period duration of nmos transistors and a second ring oscillator monitor measures a period duration of pmos transistors. A first closed control loop adaptively regulates the performance c.sub.n of the body bias controlled nmos transistors of the digital circuit by comparing the measured period duration of nmos dominated first ring oscillator monitor to a period duration of a reference clock and a second closed control loop adaptively regulating the performance c.sub.p of the body bias controlled pmos transistors of the digital circuit by comparing the measured period duration of pmos dominated second ring oscillator monitor to the period duration of the reference clock.