Mentor Graphics Corporation

Other

Siemens Industry Software Gmbh

Mentor Graphics, a Siemens Business

Wu-Tung Cheng

Yu Jane Huang

Manish Sharma

Ruifeng Guo

Janusz Rajski

Robert Brady Benware

Mark A Kassab

Huaxing Tang

Kun-Han Tsai

Liyang Lai

Xiaogang Du

Nilanjan Mukherjee

Xijiang Lin

Nagesh Tamarapalli

Gaurav Veda

Grzegorz Mrugalski

Christopher John Hill

Thomas Hans Rinderknecht

Shi-Yu Huang

Xiaoxin Fan

Joseph C Rayhawk

Omar Kebichi

Paul J Reuter

Jerzy Tyszer

Szczepan Urban

Christopher W Schuermyer

Randy Klingenberg

Don E Ross

Ting-Pu Tai

Yahya M Z Mustafa

Takeo Kobayashi

Yu Hu

Chen Wang

Jakub Janicki

Jonathan James Muirhead

Avijit Dutta

Ronald Press

Chen-Yi Chang

Jing Ye

Jay Babak Jahangiri

Mason Chern

Srinivas Patil

Martin Keim

Andreas Glowatz

Jeo-Yen Lee

Etienne Racine

Robert Randal Klingenberg

Sudhakar Mannapuram Reddy

Fan Jiang

Naixing Wang

Wei Zou

Irith Pomeranz

Chien-Chung Tsai

Omer Ghazi Samman

Yue Tian

Xiaoyuan Qi

Tzu-Heng Huang

This application discloses a computing system to identify suspected defects in a manufactured integrated circuit, which correspond to electrical failures detected by a test applied to the manufactured integrated circuit. The computing system can utilize the suspected defects in the manufactured integrated circuit to cluster features in a physical layout design describing the manufactured integrated circuit. Each cluster of the features corresponds to a candidate for a physical root cause of the suspected defects in the manufactured integrated circuit. The computing system can detect a physical root cause of the electrical failures in the manufactured integrated circuit based on the clusters of the features. A physical failure analysis process includes an inspection of the manufactured integrated circuit to confirm the physical root cause of the electrical failures in the manufactured integrated circuit corresponds to a systemic manufacturing fault in the manufactured integrated circuit.

Defect diagnosis with dynamic root cause detection

A computing system may include a model training engine configured to train a supervised learning model with a training set comprising training probability distributions computed for training dies through a local phase of a volume diagnosis procedure. The computing system may also include a volume diagnosis adjustment engine configured to access a diagnosis report for a given circuit die that has failed scan testing and compute, through the local phase of the volume diagnosis procedure, a probability distribution for the given circuit die from the diagnosis report. The volume diagnosis adjustment engine may also adjust the probability distribution into an adjusted probability distribution using the supervised learning model and provide the adjusted probability distribution for the given circuit die as an input to a global phase of the volume diagnosis procedure to determine a global root cause distribution for multiple circuit dies that have failed the scan testing.

Machine learning-based adjustments in volume diagnosis procedures for determination of root cause distributions

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.

Input data compression for machine learning-based chain diagnosis

This application discloses a computing system implementing an automatic test pattern generation tool to convert a transistor-level model of a library cell describing a digital circuit into a switch-level model of the library cell, generate test patterns configured to enable detection of target defects injected into the switch-level model of the library cell, and bifurcate the test patterns into a first subset of the test patterns and a second set of the test patterns based on detection types for the target defects enabled by the test patterns. The computing system can implement a cell model generation tool to perform an analog simulation of the transistor-level model of the library cell using the second subset of the test patterns to verify that they enable detection of target defects, while skipping performance of the analog simulation of the transistor-level model of the library cell using the first subset of the test patterns.

Library cell modeling for transistor-level test pattern generation

A circuit comprises a plurality of scan chains. The plurality of scan chains comprises bidirectional scan cells. Each of the bidirectional scan cells comprises two serial input-output ports serving as either a serial data input port or a serial data output port based on a control signal. Each of the plurality of scan chains is configured to perform a shift operation in either a first direction or a second direction based on the control signal. The first direction is opposite to the second direction.

Bidirectional scan cells for single-path reversible scan chains

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 observed failing bit patterns. Bit-reduction is performed on the observed failing bit patterns to construct first training samples. Using the first training samples, first-level machine-learning models are trained. Affine scan cell groups are identified. Second training samples are prepared for each of the affine scan cell groups by performing bit-filtering on a subset of the observed failing bit patterns associated with the faults being injected at scan cells in the each of the affine scan cell groups. Using the second training samples, second-level machine-learning models are trained. The first-level and second-level machine learning models can be applied in a multi-stage machine learning-based chain diagnosis process.

Multi-stage machine learning-based chain diagnosis

A circuit comprises: scan chains comprising scan cells, the scan chains configured to shift in test patterns, apply the test patterns to the circuit, capture test responses of the circuit, and shift out the test responses; a decompressor configured to decompress compressed test patterns into the test patterns; a test response compactor configured to compact the test responses; and shuffler circuitry inserted between outputs of the scan chains and inputs of the test response compactor, the shuffler circuitry comprising state elements configured to delay output signals from some of the scan chains for one or more clock cycles based on a control signal, the control signal varying with the test patterns.

Programmable test compactor for improving defect determination

A circuit comprises a scan chain comprising one or more multi-bit flip-flops, a plurality of multiplexers, and new scan enable signal generation circuitry. Each of the plurality of multiplexers is associated with a particular bit of the one or more multi-bit flip-flops with an output of the each of the plurality of multiplexers coupled to a data input of the particular bit, which is configured to select, based on a scan direction control signal, between an input signal from functional circuitry of the circuit and an input signal from a data output of a bit of the scan chain immediately following the particular bit in a normal scan shift direction. The new scan enable signal generation circuitry is configured to generate a new scan enable signal for the one or more multi-bit flip-flops based on the scan direction control signal and a scan enable signal for the scan chain.

Reversible multi-bit scan cell-based scan chains for improving chain diagnostic resolution

This application discloses a computing system implementing an automatic test pattern generation tool can generate test patterns to apply to a reversible scan chain in an integrated circuit. The reversible scan chain can be configured to serially load and unload the test patterns in multiple directions to generate test responses. The computing system can implement a defect diagnosis tool to detect a presence of a suspected defect associated with the reversible scan chain based on the test responses, identify which of the multiple directions used to load and unload the test patterns corresponds to the suspected defect in the reversible scan chain based on the test responses, and determine a portion of the integrated circuit to inspect for a manufacturing fault corresponding to the suspected defect based, at least in part, on the identification of which of the multiple directions corresponds to the suspected defect in the reversible scan chain.

Diagnostic resolution enhancement with reversible scan chains

A test pattern is shifted into scan chains in a circuit in a first direction. The scan cells on each of the scan chains are further coupled to corresponding scan cells on two other scan chains in the scan chains such that data bits stored in the scan cells can be shifted circularly in a second direction orthogonal to the first direction based on a control signal. The loaded test pattern is then shifted in the second direction for a number of clock cycles equal to the number of the scan chains. The test pattern is then shifted in the first direction out of the scan chains to generate a chain test result. Faulty scan cell candidates on faulty scan chains may be determined based on part of the chain test result for one of good scan chains.

Chain testing and diagnosis using two-dimensional scan architecture

A circuit comprises a plurality of scan chains configured to perform scan shifting in two opposite directions and a register configured to store a first signal. The first signal determines whether the plurality of scan chains operate in a first mode or a second mode. The plurality of scan chains operating in the first mode is configured to perform, based on a second signal, either scan shifting in a first direction in the two opposite directions or scan capturing during a test; the plurality of scan chains operating in the second mode is configured to perform, based on the second signal, scan shifting in the first direction or a second direction in the two opposite directions.

Low pin count reversible scan architecture

A system and method for performing scan chain testing is disclosed. Scan cells, in the form of scan chains, are inserted into circuit designs for testing those circuit designs. The integrity of the scan chains is checked for defects before testing the circuit under test. In order to do so, various scan chain patterns, including one or both of U-turn and Z-turn patterns, are used in order to generate scan chain test data. The scan chain test data is analyzed in order to identify one or both of a type of defect (e.g., a timing fault, stuck-at fault, etc.) or a location of the defect. Further, the scan chain testing is performed using chain patterns with adaptive length.

Optimized scan chain diagnostic pattern generation for reversible scan architecture

One, two, or three test pattern generation and encoding processes are performed for a circuit design to generate compressed test patterns for one or two input channel numbers. The one, two, or three test pattern generation and encoding processes are configured to minimize active input channels for each of the compressed test patterns. A test pattern count for each of a plurality of input channel numbers is determined based on the compressed test patterns for the one or two input channel numbers, a number of active input channels for each of the compressed test patterns, and an assumption of similar input data volumes for different numbers of input channels. The test pattern count information can be employed to determine an optimal number of input channels for a test decompressor.

Prediction of test pattern counts for scan configuration determination

Systems and methods for a deterministic automatic test generation (ATPG) process including Timing Exception ATPG (TEA). A method includes performing an automated test pattern generation (ATPG) process that uses timing exception information to generate a test pattern for a targeted fault of a circuit design with at least one timing exception path. The method includes testing the targeted fault of the circuit design using the test pattern to produce a test result for the targeted fault.

Deterministic test pattern generation for designs with timing exceptions

Various aspects of the disclosed technology relate to techniques of logic diagnosis based on cell-aware diagnostic pattern generation. A first diagnosis process is performed on a failed integrated circuit based on a first fail log to generate a first set of defect suspects. The first fail log is generated by applying the first set of test patterns to the failed integrated circuit in a first scan-based test. A second set of test patterns are generated using fault models for internal defects in one or more cells included in the first set of defect suspects. The second set of test patterns are applied to the failure integrated circuit to generate a second fail log. A second diagnosis process is performed on the failure integrated circuit based on the second fail log.

Cell-aware diagnostic pattern generation for logic diagnosis

Growing community of inventors

Lake Oswego, OR, United States of America

Wu-Tung Cheng