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This paper introduces a new metric to characterize test sets in terms of their diagnostic power. Our method uses much less space compared to the existing ones and is quite accurate. The metric can be utilized to increase the diagnosability of incompletely specified test sets via don't care filling. The X-filling approach can be integrated with test pattern generation tools to aid in better diagnostic pattern set generation. [ABSTRACT FROM PUBLISHER]

Understanding designers' intentions and accurately verifying a design are major obstacles for verification engineers today. Currently available debugging tools, such as waveform viewers, are unwieldy, often requiring the user to search through millions of cycles of logic simulation data to locate a problem. In this paper, we present Inferno, a novel solution capable of automatically extracting semantic information from a design's interface from simulation information. The semantic structure of an interface's communication protocol is presented to the user as a set of transactions, that is, monolithic communication units that have typically been observed several times during the logic simulation. Transactions can graphically be presented to the user and used as an aid to understand and validate the communication protocol of a design's interface. In addition, approved transactions can also be encoded as assertions expressed in a hardware description language (HDL) and used in constrained-random simulation to certify that the interface protocol adheres to the set of observed (and user-approved) transactions. Moreover, we developed a new closed-loop verification methodology based on Inferno, called transactional verification, which leverages approved transactions to describe correct design behavior. In our methodology, transactions are concurrently extracted during a constraint-based random simulation: the anomalous ones are flagged as potentially buggy and presented to the user for inspection. In the experimental results, we evaluate the performance and the quality of the results of Inferno on a broad range of testbench designs and several of their interfaces, including a number of communication intellectual properties and the OpenSPARC T1 8-core processor from Sun. [ABSTRACT FROM AUTHOR]

Scan chain partitioning techniques are quite effective in reducing test power, as the rippling in the clock network, scan chains, and logic is reduced altogether. Partitioning approaches implemented in a static manner may fail to reduce peak power down to the desired level, however, depending on the transition distribution of the problematic pattern in the statically constructed scan chain partitions. In this paper, we propose a dynamic partitioning approach capable of adapting to the transition distribution of any test pattern and, thus, of delivering near-perfect peak power reductions. The proposed dynamic partitioning hardware allows for the partitioning reconfiguration on a per test pattern basis, hence delivering a solution that is test set independent, yet its quality is superior to that of any test set dependent solution. [ABSTRACT FROM AUTHOR]

To locate and correct design errors that escape pre-silicon verification, silicon debug has become a necessary step in the implementation flow of digital integrated circuits. Embedded logic analysis, which employs on-chip storage units to acquire data in real time from the internal signals of the circuit-under- debug, has emerged as a powerful technique for improving observability during in-system debug. However, as the amount of data that can be acquired is limited by the on-chip storage capacity, the decision on which signals to sample is essential when it is not known a priori where the bugs will occur. In this paper, we present accelerated algorithms for restoring circuit state elements from the traces collected during a debug session, by exploiting bitwise parallelism. We also introduce new metrics that guide the automated selection of trace signals, which can enhance the real-time observability during in-system debug. [ABSTRACT FROM AUTHOR]

When hardware description languages (HDLs) are used in describing the behavior of a digital circuit, design errors (or bugs) almost inevitably appear in the HDL code of the circuit. Existing approaches attempt to reduce efforts involved in this debugging process by extracting a reduced set of error candidates. However, the derived set can still contain many error candidates, and finding true design errors among the candidates in the set may still consume much valuable time. A debugging priority method [21] was proposed to speed up the error-searching process in the derived error candidate set. The idea is to display error candidates in an order that corresponds to an individual's degree of suspicion. With this method, error candidates are placed in a rank order based on their probability of being an error. The more likely an error candidate is a design error (or a bug), the higher the rank order that it has. With the displayed rank order, circuit designers should find design errors quicker than with blind searching when searching for design errors among all the derived candidates. However, the currently used confidence score (CS) for deriving the debugging priority has some flaws in estimating the likelihood of correctness of error candidates due to the masking error situation. This reduces the degree of accuracy in establishing a debugging priority. Therefore, the objective of this work is to develop a new probabilistic confidence score (PCS) that takes the masking error situation into consideration in order to provide a more reliable and accurate debugging priority. The experimental results show that our proposed PCS achieves better results in estimating the likelihood of correctness and can indeed suggest a debugging priority with better accuracy, as compared to the CS. [ABSTRACT FROM AUTHOR]

With feature sizes steadily shrinking, manufacturing defects and parameter variations often cause design timing failures. It is essential that those errors be correctly and quickly diagnosed. In this paper, we analyze the multiple-delay-fault diagnosis problem and propose a novel approach to solve it. We enhance the diagnostic resolution by processing failure logs at various slower-than-nominal clock frequencies. We evaluate the utility of n-detection and timing-aware automatic-test-pattern-generated (ATPG) sets. Experimental results show that using timing-aware ATPG sets yields better diagnostic resolution and results in better delay-defect-size estimations compared to n-detection ATPG sets. We experimentally determined our diagnosis algorithm's sensitivity to delay variations. [ABSTRACT FROM AUTHOR]

Due to various sources of noise and process variations, assuring a circuit to operate correctly at its desired operational frequency has become a major challenge. In this paper, we propose a timing-reasoning-based algorithm and an adaptive test-generation algorithm for diagnosing timing errors in the silicon-debug phase. We first derive three metrics that are strongly correlated to the probability of a candidate's being an actual error source. We analyze the problem of circuit timing uncertainties caused by delay variations and test sampling. Then, we propose a candidate-ranking heuristic, which is robust with respect to such sources of timing uncertainty. Based on the initial ranking result and the timing information, we further propose an adaptive path-selection and test-generation algorithm to generate additional diagnostic patterns for further improvement of the first-hit-rate. The experimental results demonstrate that combining the ranking heuristic and the adaptive test-generation method would result in a very high resolution for timing diagnosis. [ABSTRACT FROM AUTHOR]