45992450
XMAX: X-tolerant architecture for MAXimal test compression
XMAX is a novel test data compression architecture capable of achieving almost exponential reduction in scan test data volume and test time while allowing use of commercial automatic test pattern generation (ATPG) tools. It tolerates presence of sources of unknown logic values (also referred to as X's) without compromising test quality and diagnosis capability for most practical purposes. The XMAX architecture has been implemented in several industrial designs.
900289
Techniques and algorithms for fault grading of FPGA interconnect test configurations
Conventional fault simulation techniques for field programmable gate arrays (FPGAs) are very complicated and time consuming. The alternative, FPGA fault emulation technique, is incomplete and can be used only after the FPGA chip is manufactured. In this paper, we present efficient algorithms for computing the fault coverage of a given FPGA test configuration. The faults considered are opens and shorts in FPGA interconnects. The presented technique is able to report all detectable and undetectable faults and, compared with conventional methods, is orders of magnitude faster.
18607273
X-tolerant test response compaction
Larger, denser designs lead to more defects; higher quality requirements and new test methods lead to an explosion in test data volume. Test compression technique attempt to do more testing with fewer bits. This article summarizes one such method, X compact which addresses how unknowns the bane of compression and logic BIST techniques are eliminated. DFT engineers must spend serious effort to minimize Xs in future designs. It is impossible to eliminate all Xs. X-tolerant response compactors are necessary for tolerating residual Xs to enable massive compaction with practically no impact on test quality. X-compactors are mainly useful for test compression purposes and provide up to 80 times the test response compaction of traditional scan. X-tolerant signature analyzers extend the X-compact concept to incorporate time compaction, thereby tolerating thousands of Xs and reducing test response data volume by 50 to 2,000 times relative to traditional scan. These signature analyzers are extremely beneficial for BIST because Xs can easily corrupt traditional MISR-based BIST signature analyzers. X-tolerant response compactors also enable efficient diagnosis essential to fast yield-learning.
2397835
Speed clustering of integrated circuits
Experimental data on 0.18 /spl mu/ test chips shows strong evidence of clustering of speeds of neighboring dies on a wafer. This clustering phenomenon is utilized to develop techniques for predicting the speed of a part from the speeds of three or more of its neighbors. On-chip processor monitors are used to further improve the prediction accuracy of these techniques. Experimental data demonstrates both the effectiveness of these prediction schemes and the possibility of applying of them to reduce the cost of speed binning.
2705949
Logic soft errors in sub-65nm technologies design and CAD challenges
Logic soft errors are radiation induced transient errors in sequential elements (flip-flops and latches) and combinational logic. Robust enterprise platforms in sub-65nm technologies require designs with built-in logic soft error protection. Effective logic soft error protection requires solutions to the following three problems: (1) accurate soft error rate estimation for combinational logic networks; (2) automated estimation of system effects of logic soft errors, and identification of regions in a design that must be protected; and, (3) new cost-effective techniques for logic soft error protection, because classical fault-tolerance techniques are very expensive.
2734062
A design diversity metric and reliability analysis for redundant systems
Design diversity has long been used to protect redundant systems against common-mode failures. The conventional notion of diversity relies on "independent" generation of "different" implementations. This concept is qualitative and does not provide a basis to compare the reliabilities of two diverse systems. In this paper, for the first time, we present a metric to quantify diversity among several designs. Based on this metric, we derive analytical reliability models that show a simple relationship between design diversity, system failure rate, and mission time. In addition, we present simulation results to demonstrate the effectiveness of design diversity in Duplex and Triple Modular Redundant (TMR) systems. For independent multiple-module failures, we show that, mere use of different implementations does not always guarantee higher reliability compared to redundant systems with identical implementations-it is important to analyze the reliability of redundant systems using our metric. For common-mode failures and design faults, there is a significant gain in using different implementations-however, as our analysis shows, the gain diminishes as the mission time increases. Our simulation results also demonstrate the usefulness of diversity for enhancing the self-testing properties of redundant systems.
35096806
Human Face Identification from Video Based on Frequency Domain Asymmetry Representation Using Hidden Markov Models
In this paper we introduce a novel human face identification scheme from video data based on a frequency domain representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.
9622081
Modular Rough Fuzzy MLP: Evolutionary Design
The article describes a way of designing a hybrid system for classification and rule generation, integrating rough set theory with a fuzzy MLP using an evolutionary algorithm. An l-class classification problem is split into l two-class problems. Crude subnetworks are initially obtained for each of these two-class problems via rough set theory. These subnetworks are then combined and the final network is evolved using a GA with restricted mutation operator which utilizes the knowledge of the modular structure already generated, for faster convergence.
45398591
A One Bit Facial Asymmetry Code (FAC) in Fourier Domain for Human Recognition
The present paper introduces a novel set of biometrics based on facial asymmetry measures in the frequency domain using a compact one-bit representation. A simplistic Hamming distance-type classifier is proposed as a means for matching bit patterns for identification purposes which is more efficient than PCA-based classifiers from storage and computation point of view, and produces equivalent results. A comparison with spatial intensity-based asymmetry measures suggests that our proposed measures are more robust to intra-personal distortions with a misclassification rate of only 4.24% on the standard facial expression database (Cohn-Kanade) consisting of 55 individuals. In addition, a rigorous statistical analysis of the matching algorithm is presented. The role of asymmetry of different face parts (e.g., eyes, mouth, nose) is investigated to determine which regions provide the maximum discrimination among individuals under different expressions.
62742629
Local facial asymmetry for expression classification
We explore a novel application of facial asymmetry: expression classification. Using 2D facial expression images, we show the effectiveness of automatically selected local facial asymmetry for expression recognition. Quantitative evaluations of expression classification using local asymmetry demonstrate statistically significant improvements over expression classification results on the same data set without explicit representation of facial asymmetry. A comparison of discriminative local facial asymmetry features for expression classification versus human identification is given.
10906898
The Role of Statistical Models in Biometric Authentication
The current paper demonstrates the role of statistical models in authentication tasks – both in system development and in performance evaluation. We first introduce a model-based face authentication system based on the Fourier domain phase using Gaussian Mixture Models (GMM) which yields verification error rates as low as 0.3% on a face database of 65 individuals with extreme illumination variations. We then present a statistical framework for predicting authentication error rates for future populations in a rigorous way. This is in contrast to most evaluation protocols used today that are based on observational studies and valid only for the databases at hand. Applications establish that our model-based approach has better predictive performance than an existing state-of-the-art authentication technique.