Static Test Pattern Research Articles

A DFT-Compatible In-Situ Timing Error Detection and Correction Structure Featuring Low Area and Test Overhead

In-situ timing error detection and correction (EDAC) structure is widely adopted in timing-error resilient circuits to reduce the conservative timing guardband induced by process, voltage, and temperature (PVT) variations. However, it introduces the latch-based datapath as well as extra detection and propagation logic, therefore challenges the design-for-testability (DFT) implementation. In this article, we propose a novel DFT-compatible EDAC structure with significant signal control simplification and test-pattern complexity reduction, featuring low area and test overhead. This structure leverages a new scannable EDAC cell (SEDC) which can be configured for timing EDAC in normal mode, or for shift operations as a flip-flop in scan mode. Specifically, the proposed detection logic can be controlled succinctly in scan shift operations and then observed via the global error propagation logic with simple control signal configurations during the test. Therefore, the sophisticated test pattern generation and critical path sensitization are removed. Based on the structure, a shift-based test method is presented to cover the EDAC structure with a low test pattern complexity and test time overheads. As compared with previous works, the proposed SEDC saves 30.5% area, 16.6% power, and 20.3% delay. Besides, our test method cooperating with the proposed EDAC structure reduces <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$149\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$23\times $ </tex-math></inline-formula> static and at-speed test patterns, respectively, together with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$232\times $ </tex-math></inline-formula> static test cycles, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25\times $ </tex-math></inline-formula> at-speed test cycles on average, which proves the effectiveness for DFT.

Dynamic Statistical-Timing-Analysis-Based VLSI Path Delay Test Pattern Generation

Nanoscale VLSI systems are subject to increasingly significant performance variability. Accurate timing analysis and effective silicon-based performance verification techniques are critical to successful nanoscale VLSI design. The state-of-the-art statistical static timing analysis (SSTA) techniques cannot capture performance variability due to primary inputs and sequential element states which, however, is critical to path delay test generation. In this paper, we present the first dynamic statistical timing analysis-based VLSI path delay test pattern generation technique. We observe that VLSI timing analysis and power estimation target the same signal toggling activity. By leveraging the existing power estimation techniques, we have developed signal-probability-based statistical timing analysis (SPSTA), and SPSTA-based VLSI delay test pattern generation (SPSTA-DTPG) techniques. Our experimental results based on ISCAS’89 benchmark circuits show that the state-of-the-art statistical static timing analysis-based delay test pattern generation (SSTA-DTPG) achieves an average of 47.32%, 45.14%, and 57.98%, SPSTA-DTPG achieves an average of 57.41%, 61.43%, and 68.05%, while signal probability-based statistical timing analysis-based delay test pattern generation with (test pattern) compaction (SPSTA-DTPG-C) achieves an average of 83.09%, 87.48% and 90.30% coverage of the top 50, 100, and 200 timing-critical paths, respectively.

Motion-aftereffect-induced blindness

Motion-induced blindness (MIB) describes the occasional disappearance of salient visual objects in the presence of moving features (Y. S. Bonneh, A. Cooperman, & D. Sagi, 2001). Here we test whether motion adaptation and the ensuing motion aftereffect (MAE) are sufficient to trigger disappearance of salient targets. In three experiments, observers adapted to either rotating or static stimuli. Immediately afterwards, a static test pattern was presented consisting of a mask with texture elements and three superimposed target dots in a triangular arrangement. Observers reported dot disappearance and reappearance. The results clearly show that illusory motion in a static test pattern, following motion adaptation, promotes the disappearance of target dots. Furthermore, disappearance is modulated by the depth relationship between test pattern and targets, increasing for targets placed stereoscopically behind the test pattern. We conclude that MIB is influenced by perceived relative motion between depth-segregated features.

Contrast sensitivity of digital imaging display systems: Contrast threshold dependency on object type and implications for monitor quality assurance and quality control in PACS

The American Association of Physicists in Medicine Task Group 18 has published standards and quality control (QC) guidelines to ensure consistency and optimal quality for digital image display systems (DIDSs). In many of these recommended QC tests, static test patterns that contain low-contrast objects are often used to assess and validate the quality of a DIDS. These low-contrast objects often have the shape of circular disks or squares with sharp edges, neither of which resemble most of the diagnostic findings in medical images. On the other hand, circular objects with fuzzy boundaries bear a closer resemblance to lung nodules in chest radiography and masses in mammography; thus, they may be more clinically relevant in assessing display system quality. In this article human observers' contrast sensitivities of circular objects with sharp edges and those with fuzzy ones were investigated. The contrast thresholds of human viewers using a consumer-grade color LCD monitor and a medical-grade monochrome LCD monitor were measured for objects of various sizes displayed against uniform backgrounds with various luminance levels. Contrast-detail curves for circular objects with sharp edges and those with fuzzy boundaries were measured and compared. It was found that contrast thresholds for objects with fuzzy boundaries were higher (i.e., the objects were more difficult to detect) than those with sharp edges. Objects with fuzzy boundaries were potentially more sensitive in distinguishing quality differences among image display devices and thus may be a better QC measurement in detecting subtle deterioration in image display devices.

Analytic IMRT dose calculations utilizing Monte Carlo to predict MLC fluence modulation

A hybrid dose-computation method is designed which accurately accounts for multileaf collimator (MLC)-induced intensity modulation in intensity modulated radiation therapy (IMRT) dose calculations. The method employs Monte Carlo (MC) modeling to determine the fluence modulation caused by the delivery of dynamic or multisegmental (step-and-shoot) MLC fields, and a conventional dose-computation algorithm to estimate the delivered dose to a phantom or a patient. Thus, it determines the IMRT fluence prediction accuracy achievable by analytic methods in the limit that the analytic method includes all details of the MLC leaf transport and scatter. The hybrid method is validated and benchmarked by comparison with in-phantom film dose measurements, as well as dose calculations from two in-house, and two commercial treatment planning system analytic fluence estimation methods. All computation methods utilize the same dose algorithm to calculate dose to a phantom, varying only in the estimation of the MLC modulation of the incident photon energy fluence. Gamma analysis, with respect to measured two-dimensional (2D) dose planes, is used to benchmark each algorithm's performance. The analyzed fields include static and dynamic test patterns, as well as fields from ten DMLC IMRT treatment plans (79 fields) and five SMLC treatment plans (29 fields). The test fields (fully closed MLC, picket fence, sliding windows of different size, and leaf-tip profiles) cover the extremes of MLC usage during IMRT, while the patient fields represent realistic clinical conditions. Of the methods tested, the hybrid method most accurately reproduces measurements. For the hybrid method, 79 of 79 DMLC field calculations have gamma < 1 (3%/3 mm) for more than 95% of the points (per field) while for SMLC fields, 27 of 29 pass the same criteria. The analytic energy fluence estimation methods show inferior pass rates, with 76 of 79 DMLC and 24 of 29 SMLC fields having more than 95% of the test points with gamma < or = 1 (3%/3 mm). Paired one-way ANOVA tests of the gamma analysis results found that the hybrid method better predicts measurements in terms of both the fraction of points with gamma < or = 1 and the average gamma for both 2%/2 mm and 3%/3 mm criteria. These results quantify the enhancement in accuracy in IMRT dose calculations when MC is used to model the MLC field modulation.

The motion aftereffect of transparent motion: Two temporal channels account for perceived direction

Adaptation to orthogonal transparent patterns drifting at the same speed produces a unidirectional motion aftereffect (MAE) whose direction is opposite the average adaptation direction. If the patterns move at different speeds, MAE direction can be predicted by an inverse vector average, using the observer’s motion sensitivity to each individual pattern as vector magnitudes. These weights are well approximated by the duration of each pattern’s MAE, as measured with static test patterns. However, previous efforts to use the inverse-vector-average rule with dynamic test patterns have failed. Generally, these studies have used spatially and temporally broadband test stimuli. Here, in order to gain insight into the possible contribution of temporal channels, we filtered our test pattern in the temporal domain to produce five ideal, octave-width pass-bands. MAE durations were measured for single-component stimuli drifting at various adaptation speeds and tested at a range of temporal frequencies. Then, two components with orthogonal directions and different speeds were combined and the direction of the resulting MAE was measured. The key findings are that: (i) for a given adaptation speed, the duration of a single component’s MAE is dependent on test temporal frequency; (ii) the direction of MAEs produced by transparent motion (i.e., bivectorial adaptation) also varies strongly as a function test temporal frequency (by up to 90° for some speed pairings); and (iii) the inverse-vector-average rule predicts the direction of the transparent MAE provided the MAE durations used to weight the vector combination were obtained from stimuli matched in adaptation speed and test temporal frequency. These results are discussed in terms of the number and shape of temporal channels in our visual system.

Influence of viewing distance on aftereffects of moving random pixel arrays

Viewing-distance invariance of visual perception has evolutionary advantages, but it is of necessity limited by spatial and temporal resolution. Even within these resolution limits viewing-distance invariance might not be perfect or even good, but there are remarkably few studies of its precise limits. Here we ask to what extent viewing-distance invariance holds for motion aftereffects (MAEs). There are (at least) two different MAEs: one can be seen on a static test pattern (sMAE) and is tuned to low speeds, the other only becomes manifest on a dynamic noise test stimulus (dMAE) and is sensitive to higher adaptation speeds. We show that each of these MAEs has a limited viewing-distance invariance, the dMAE only for higher screen-speeds and the sMAE only for lower screen-speeds. In both cases upper angular-speed limits shift to higher values for smaller viewing-distances (lower spatial frequencies, larger fields). This upper limit is constant, independent of viewing distance, if expressed in terms of screen-speed. On the other hand the lower speed limit is fixed in angular-speed and variable in screen-speed terms. Explanations for these findings are provided. We show that there is no fixed optimum viewing-distance or optimum angular stimulus-size for either of the two MAEs.

Slow and fast visual motion channels have independent binocular-rivalry stages.

We have previously reported a transparent motion after-effect indicating that the human visual system comprises separate slow and fast motion channels. Here, we report that the presentation of a fast motion in one eye and a slow motion in the other eye does not result in binocular rivalry but in a clear percept of transparent motion. We call this new visual phenomenon 'dichoptic motion transparency' (DMT). So far only the DMT phenomenon and the two motion after-effects (the 'classical' motion after-effect, seen after motion adaptation on a static test pattern, and the dynamic motion after-effect, seen on a dynamic-noise test pattern) appear to isolate the channels completely. The speed ranges of the slow and fast channels overlap strongly and are observer dependent. A model is presented that links after-effect durations of an observer to the probability of rivalry or DMT as a function of dichoptic velocity combinations. Model results support the assumption of two highly independent channels showing only within-channel rivalry, and no rivalry or after-effect interactions between the channels. The finding of two independent motion vision channels, each with a separate rivalry stage and a private line to conscious perception, might be helpful in visualizing or analysing pathways to consciousness.

Motion aftereffect of combined first-order and second-order motion.

When, after prolonged viewing of a moving stimulus, a stationary (test) pattern is presented to an observer, this results in an illusory movement in the direction opposite to the adapting motion. Typically, this motion aftereffect (MAE) does not occur after adaptation to a second-order motion stimulus (i.e. an equiluminous stimulus where the movement is defined by a contrast or texture border, not by a luminance border). However, a MAE of second-order motion is perceived when, instead of a static test pattern, a dynamic test pattern is used. Here, we investigate whether a second-order motion stimulus does affect the MAE on a static test pattern (sMAE), when second-order motion is presented in combination with first-order motion during adaptation. The results show that this is indeed the case. Although the second-order motion stimulus is too weak to produce a convincing sMAE on its own, its influence on the sMAE is of equal strength to that of the first-order motion component, when they are adapted to simultaneously. The results suggest that the perceptual appearance of the sMAE originates from the site where first-order and second-order motion are integrated.

Integration after adaptation to transparent motion: static and dynamic test patterns result in different aftereffect directions.

One of the many interesting questions in motion aftereffect (MAE) research is concerned with the location(s) along the pathway of visual processing at which certain perceptual manifestations of this illusory motion originate. One such manifestation is the unidirectionality of the MAE after adaptation to moving plaids or transparent motion. This unidirectionality has led to the suggestion that the origin of this MAE might be a single source (gain control) located at, or beyond areas that are believed to be responsible for the integration of motion signals. In this report we present evidence against this suggestion using a simple experiment. For the same adaptation pattern, which consisted of two orthogonally moving transparent patterns with different speeds, we show that the direction of the resulting unidirectional MAE depends on the nature of the test stimulus. We used two kinds of test patterns: static and dynamic. For exactly the same adaptation conditions, the difference in MAE direction between testing with static and dynamic patterns can be as large as 50 degrees. This finding suggests that this MAE is not just a perceptual manifestation of a passive recovery of adapted motion sensors but an active integrative process using the output of different gain controls. A process which takes place after adaptation. These findings are in line with the idea that there are several sites of adaptation along the pathway of visual motion processing and that the nature of the test pattern determines the fate of our perceptual experience of the MAE.

Aftereffect of high-speed motion.

A visual illusion known as the motion aftereffect is considered to be the perceptual manifestation of motion sensors that are recovering from adaptation. This aftereffect can be obtained for a specific range of adaptation speeds with its magnitude generally peaking for speeds around 3 deg s-1. The classic motion aftereffect is usually measured with a static test pattern. Here, we measured the magnitude of the motion aftereffect for a large range of velocities covering also higher speeds, using both static and dynamic test patterns. The results suggest that at least two (sub)populations of motion-sensitive neurons underlie these motion aftereffects. One population shows itself under static test conditions and is dominant for low adaptation speeds, and the other is prevalent under dynamic test conditions after adaptation to high speeds. The dynamic motion aftereffect can be perceived for adaptation speeds up to three times as fast as the static motion aftereffect. We tested predictions that follow from the hypothesised division in neuronal substrates. We found that for exactly the same adaptation conditions (oppositely directed transparent motion with different speeds), the aftereffect direction differs by 180 degrees depending on the test pattern. The motion aftereffect is opposite to the pattern moving at low speed when the test pattern is static, and opposite to the high-speed pattern for a dynamic test pattern. The determining factor is the combination of adaptation speed and type of test pattern.

What is the Transition Point between Static and Dynamic Motion Aftereffects?

The motion aftereffect (MAE) measured with a dynamic test pattern (eg a counterphase-flickering grating) is distinguishable by a number of properties from the classical MAE obtained with a static test pattern. For a dynamic MAE, however, it is not sufficient simply to introduce dynamic properties into the test pattern. In two experiments we attempted to determine the transition point in the temporal-frequency domain at which a dynamic MAE becomes distinguishable from the static MAE. First, we examined the interocular transfer (IOT) of the MAE with conventional first-order (luminance) gratings. The amount of IOT increased with temporal frequency, and was almost complete at 1 Hz and above. In addition, the IOT of a dynamic MAE shows a drastic reduction in the peripheral visual field, possibly reflecting difficulties in feature tracking or the loss of involuntary attention. Second, we examined the MAE with second-order motion as the adaptation stimulus (contrast modulation of two-dimensional static noise). Under these conditions, similar results were obtained for first-order and second-order test gratings: MAE was not observed at low temporal frequencies and a substantial MAE was observed only at 1 Hz and above. The results agree with recent findings which showed a gradual loss of spatial-frequency selectivity with increasing temporal frequency of the test pattern (Mareschal et al, 1997 Vision Research37 1755 – 1759). The present results support the idea that two mechanisms underlie the different kinds of MAE: a low-level mechanism responsible for the MAE observed at low temporal frequencies, and a high-level mechanism operating predominantly at high temporal frequencies with a transition point at about 1 Hz.

Temporal and spatial frequency tuning of the flicker motion aftereffect.

The motion aftereffect (MAE) was used to study the temporal and spatial frequency selectivity of the visual system at supra-threshold contrasts. Observers adapted to drifting sine-wave gratings of a range of spatial and temporal frequencies. The magnitude of the MAE induced by the adaptation was measured with counterphasing test gratings of a variety of spatial and temporal frequencies. Independently of the spatial or temporal frequency of the adapting grating, the largest MAE was found with slowly counterphasing test gratings (at approximately 0.125-0.25 Hz). The largest MAEs were also found when the test grating was of similar spatial frequency to that of the adapting grating, even at very low spatial frequencies (0.125 c/deg). These data suggest that MAEs are dominated by a single, low-pass temporal frequency mechanism and by a series of band-pass spatial frequency mechanisms. The band-pass spatial frequency tuning even at low spatial frequencies suggests that the "lowest adaptable channel" concept [Cameron et al. (1992). Vision Research, 32, 561-568] may be an artifact of disadvantaged low spatial frequencies using static test patterns.

Recovery from adaptation for dynamic and static motion aftereffects: Evidence for two mechanisms

The motion aftereffect (MAE) is an illusory drift of a physically stationary pattern induced by prolonged viewing of a moving pattern. Depending on the nature of the test pattern the MAE can be phenomenally different. This difference in appearance has led to the suggestion that different underlying mechanisms may be responsible and several reports show that this might be the case. Here, we tested whether differences in MAE duration obtained with stationary test patterns and dynamic test patterns can be explained by a single underlying mechanism. We find the results support the existence of (at least) two mechanisms. The two mechanisms show different characteristics: the static MAE (i.e. the MAE tested with a static test pattern) is almost completely stored when the static test is preceded by a dynamic test; in contradistinction, the dynamic MAE is not stored when dynamic testing is preceded by a static test pattern.

Complete interocular transfer of motion aftereffect with flickering test

It has been suggested that motion aftereffect with static test patterns (static MAE) reflects activities at a lower level system that dominantly processes first-order motion, while MAE with a directionally ambiguous test (flicker MAE) reveals a higher level system where second-order motion signals as well as first-order signals are available. To test this hypothesis, we examined interocular transfer of static and flicker MAE. Flicker MAE should transfer more efficiently than static MAE if it occurs at a higher level system. In the first experiment, the adaptation stimulus was a drifting luminance grating (first-order motion), or a drifting grating defined by flicker or texture difference (second-order motion). The test stimulus was a luminance grating, either static or counterphasing. The results indicated that static MAE, which was induced only by first-order motion, transferred partially, as has been reported in previous studies, but the transfer of flicker MAE was nearly perfect with either first- or second-order adaptation stimuli. The second experiment with varied adaptation contrast indicated that this complete transfer was not due to a ceiling effect. These results supported the hypothesis that the underlying mechanism for flicker MAE is located at a level higher than the mechanism for static MAE.

CMOS transistor faults and bridging faults: Testability by delay effects and overcurrents

Testing CMOS circuits for faults which cannot safely be detected by static test patterns has been a matter of intense research for years. Recently methods based on delay fault models have been developed for a safe detection of dynamic faults. At the same time, test methods based on overcurrent effects have found new interest due to the application of built-in current monitoring elements. This paper analyses some typical types of defects in CMOS circuits for their detectability either by delay effects or overcurrents. Advances and limitations of both basic approaches are discussed.