Transition Faults and Transition Path Delay Faults: Test Generation, Path Selection, and Built-In Generation of Functional Broadside Tests

As the clock frequency and complexity of digital integrated circuits increase rapidly, delay testing is indispensable to guarantee the correct timing behavior of the circuits. In this dissertation, we describe methods developed for three aspects of delay testing in scan-based circuits: test generation, path selection and built-in test generation. We first describe a deterministic broadside test generation procedure for a path delay fault model named the transition path delay fault model, which captures both large and small delay defects. Under this fault model, a path delay fault is detected only if all the individual transition faults along the path are detected by the same test. To reduce the complexity of test generation, sub-procedures with low complexity are applied before a complete branch-and-bound procedure. Next, we describe a method based on static timing analysis to select critical paths for test generation. Logic conditions that are necessary for detecting a path delay fault are considered to refine the accuracy of static timing analysis, using input necessary assignments. Input necessary assignments are input values that must be assigned to detect a fault. The method calculates more accurate path delays, selects paths that are critical during test application, and identifies undetectable path delay faults. These two methods are applicable to off-line test generation. For large circuits with high complexity and frequency, built-in test generation is a cost-effective method for delay testing. For a circuit that is embedded in a larger design, we developed a method for built-in generation of functional broadside tests to avoid excessive power dissipation during test application and the overtesting of delay faults, taking the functional constraints on the primary input sequences of the circuit into consideration. Functional broadside tests are scan-based two-pattern tests for delay faults that create functional operation conditions during test application. To avoid the potential fault coverage loss due to the exclusive use of functional broadside tests, we also developed an optional DFT method based on state holding to improve fault coverage. High delay fault coverage can be achieved by the developed method for benchmark circuits using simple hardware

A survey of scan-capture power reduction techniques

With the advent of sub-nanometer geometries, integrated circuits (ICs) are required to be checked for newer defects. While scan-based architectures help detect these defects using newer fault models, test data inflation happens, increasing test time and test cost. An automatic test pattern generator (ATPG) exercise’s multiple fault sites simultaneously to reduce test data which causes elevated switching activity during the capture cycle. The switching activity results in an IR drop exceeding the devices under test (DUT) specification. An increase in IR-drop leads to failure of the patterns and may cause good DUTs to fail the test. The problem is severe during at-speed scan testing, which uses a functional rated clock with a high frequency for the capture operation. Researchers have proposed several techniques to reduce capture power. They used various methods, including the reduction of switching activity. This paper reviews the recently proposed techniques. The principle, algorithm, and architecture used in them are discussed, along with key advantages and limitations. In addition, it provides a classification of the techniques based on the method used and its application. The goal is to present a survey of the techniques and prepare a platform for future development in capture power reduction during scan testing

Enhancement and validation of a test technique for integrated circuits

This thesis focuses on a scan-based delay testing technique that was recently developed at ETS. This new approach, called Captureless Delay Testing (CDT), has been proposed as a technique that complements traditional methods of test, ensuring the integrated circuits will function at their proposed clock speed, further improving the test coverage of the particular type of test. Furthermore, CDT incorporates the use of sensors enabling the detection of the presence of transitions at strategic locations. The purpose of this project is to improve on certain aspects of this novel technique. At first, we analyze the delay distribution of the non-covered nodes by traditional methods of test, in order to develop the best way possible of placement of the CDT sensors. We present, using Perl Language, the ensemble of tools developed for this purpose. The end results obtained confirm that the paths that pass through the non-covered nodes are longer than those that traverse the covered ones. The difference between the two types of paths exceeds 20%) of the clock period when considering the shorter path delay values. Secondly, we propose a fially automated algorithm that enables, at the earliest stages of the test vectors generation process: 1) the identification of the non-covered nodes, 2) the identification of the placements of the CDT sensors at the inputs of the flip-flops for further improvement of the test coverage, and 3) the minimization of the number of sensors with regards to requirements. Our results indicate that when we apply CDT on top of transitionbased fault model we can improve the test coverage by 5%. Moreover, the algorithm of CDT sensors minimization allows a reduction of more than 85% the number of those sensors with a minimal test coverage loss, on average of 1.6%

Acoustic Measurement of Snow

Instrumentation commonly used to measure snowpack stratigraphy, snow density, Snow Water Equivalent (SWE), temperature and liquid water content is usually invasive and requires disruption of the snowpack. Most measurement techniques modify the snow medium and more than one sample cannot be taken at the same location. This does not permit continuous monitoring of these parameters using a single measurement instrument. An acoustic wave sent into the snowpack was used to measure snow. To provide the theory required to make acoustic measurements, the Biot-Stoll model of sound wave propagation in porous media was modified using a mixture theory so that it was applicable to a multiphase porous medium. The combined model is called the Unified Thermoacoustic Model (UTAM) for snow. An acoustic measurement device, the System for the Acoustic Sensing of Snow (SAS2), was designed to send sound waves into snow and to receive the reflected sound waves using a loudspeaker and a microphone array. A stationary version of the SAS2 was deployed on a met station and a portable version of the SAS2 was placed on a roving ski-based platform. The systems were deployed at field sites in the Canadian Rocky Mountains, Alberta. The results showed that the SAS2 was able to measure snow density, temperature, and liquid water content and serve as a replacement technology for snowtube and snowpit measurements. Snow density was estimated more accurately by the SAS2 than from commonly-used snow tube techniques

Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments