Described herein are systems, methods, and other techniques for identifying redundant parameters and reducing parameters for testing a device. A set of test values and limits for a set of parameters are received. A set of simulated test values for the set of parameters are determined based on one or more probabilistic representations for the set of parameters. The one or more probabilistic representations are constructed based on the set of test values. A set of cumulative probabilities of passing for the set of parameters are calculated based on the set of simulated test values and the limits. A reduced set of parameters are determined from the set of parameters based on the set of cumulative probabilities of passing. The reduced set of parameters are deployed for testing the device.

A core partition circuit comprises a first decompression circuit, a second decompression circuit, a first switching circuit, an wrapper scanning circuit, a first compression circuit, a second compression circuit and a second switching circuit. The first and second decompression circuits decompress an input signal. The first switching circuit outputs the output signal of the first decompression circuit or the second decompression circuit according to a first control signal. The wrapper scanning circuit receives the output signal of the first decompression circuit or the second decompression circuit to scan the internal or the port of the core partition circuit. The first and second compression circuits respectively compress the internal logic and the port logic of the core partition circuit. The second switching circuit outputs the compressed internal logic or port logic of the core partition circuit according to the first control signal.

A test and measurement device includes an input for receiving a test waveform from a Device Under Test (DUT), where the test waveform has a plurality of input level transitions, a selector structured to respectively and individually extract only those portions of the test waveform that match two or more predefined patterns of input level transitions of the test waveform, a noise compensator structured to individually determine and remove, for each of the extracted portions of the waveform, a component of a jitter measurement caused by random noise of the test and measurement device receiving the test waveform, a summer structured to produce a composite distribution of timing measurements with removed noise components from the extracted portions of the test waveform, and a jitter processor structured to determine a first noise-compensated jitter measurement of the DUT from the composite distribution. Methods of determining noise-compensated jitter measurements are also disclosed.

Silicon test structures are described that enable separate measurement of n-channel metal-oxide semiconductor (NMOS) and p-channel metal-oxide semiconductor (PMOS) transistor delays. NMOS and PMOS specific non-inverting stages may be used to construct a multi-stage ring oscillator. Each of the non-inverting stages generates either a rising or falling primary transition that is determined by either NMOS or PMOS transistors, respectively. The opposing transition for a particular non-inverting stage is triggered by propagation of the primary transition to a subsequent non-inverting stage (producing a “reset” pulse). A frequency of the ring oscillator is determined by the primary transition and one transistor type (NMOS or PMOS). Specifically, the frequency is determined by the propagation delay of the primary transition through the entire ring oscillator.

Test stimulus signals applied to at least one circuit under test are produced in a set of test stimulus generators as a function of test stimulus information loaded in test stimulus registers. Loading of the test stimulus information in the test stimulus registers is controlled as a function of test programming information loaded via a programming interface in a respective control register in a set of control registers. The test stimulus generators are activated as a function of the test programming information loaded in said control registers. Test outcome signals received from the at least one circuit under test are used to produce signature comparison signals, which are compared with respective programmable signature reference signals stored in a set of input signature registers, are produced in response to the signature comparison signals produced from the test outcome signals failing to match with the respective reference signals.

A method for evaluating tests for fabricated integrated circuit (IC) chips includes providing, design for fault injection (DfFI) instances of an IC design that characterize activatable states of controllable elements in an IC chip based on the IC design. The method also includes fault simulating the IC design a corresponding identified test suite to determine a signature for faults and simulating the IC design with the DfFI instances activated to determine a signature for the DfFI instances. The method includes generating a DfFI-fault equivalence dictionary based on a comparison of the signature of the faults and DfFI instances and generating tests for a fabricated IC chip based on the IC design. The method includes receiving test result data characterizing the tests being applied against the fabricated IC chip with the DfFI instances activated and analyzing the test result data to determine an ability of the tests to detect the faults.

A device for printing to a recording medium with an inkjet printing unit that has at least one nozzle arrangement and is designed to generate a print image on the recording medium. The print image includes at least one test pattern that exhibits at least two different spatial frequencies. The device also has an image acquisition unit that is designed to acquire an image of an acquisition region on the recording medium, which acquisition region includes at least a portion of the test pattern; and a processor that is designed to generate image data corresponding to the image and determine a functional state of the nozzle arrangement, by means of the image data, using a neural network.

In one embodiment, an apparatus includes at least one fabric to interface with a plurality of intellectual property (IP) blocks of the apparatus, the at least one fabric including at least one status storage, and a fabric bridge controller coupled to the at least one fabric. The fabric bridge controller may be configured to initiate a functional safety test of the at least one fabric in response to a fabric test signal received during functional operation of the apparatus, receive a result of the functional safety test via the at least one status storage, and send to a destination location a test report based on the result. Other embodiments are described and claimed.

The signal toggling detection and correction circuit includes a flip-flop, a checker circuit, and a fault monitoring circuit that includes a restoration circuit. Based on faults such as soft errors and unintended bit toggles in the flip-flop, a flop output signal toggles. A set of checker signals outputted by the checker circuit may toggle based on toggling of the flop output signal and a restoration signal of the restoration circuit. Based on the toggling of at least one checker signal, the fault monitoring circuit determines whether the flip-flop or the checker circuit is faulty. When the checker circuit is faulty, the fault monitoring circuit corrects the toggling of at least one checker signal. When the flip-flop is faulty, the fault monitoring circuit corrects the toggling of one of the toggled flop output signal or the restoration signal and further corrects the toggled checker signal.

Various aspects of the disclosed technology relate to machine learning-based chain diagnosis. Faults are injected into scan chains in a circuit design. Simulations are performed on the fault-injected circuit design to determine test response patterns in response to the test patterns which are captured by the scan chains. Observed failing bit patterns are determined by comparing the unloaded test response patterns with corresponding good-machine test response patterns. Bit-reduction is performed on the observed failing bit patterns to construct training samples. Using the training samples, machine-learning models for faulty scan cell identification are trained. The bit reduction comprises pattern-based bit compression for good scan chains or cycle-based bit compression for the good scan chains. The bit reduction may further comprise bit-filtering. The bit-filtering may comprises keeping only sensitive bits on faulty scan chains for the training samples construction.