Acceleration of Seed Ordering and Selection For High Quality VLSI Delay Test

Seed ordering and selection is a key technique to provide high-test quality with limited resources in Built-In Self Test (BIST) environment. We present a hard-to-detect delay fault selection method to optimize the computation time in seed ordering and selection processes. This selection method can be used to select faults for test generation when it is impractical to target all delay faults resulting large test pattern count and long Computation time. Three types of selection categories are considered, ranged in the number of seeds it produced, which is useful when we consider computing resources, such as memory and storage. We also evaluate the impact of the selection method in mixed-mode BIST when seed are expanded to more patterns, and evaluate the statistical delay quality level (SDQL) with the original work. Experimental results show that our proposed method can significantly reduce computation time while slightly sacrificing test quality

Acceleration of Seed Ordering and Selection for High Quality Delay Test

Seed ordering and selection is a key technique to provide high-test quality with limited resources in Built-In Self Test (BIST) environment. We present a hard-to-detect delay fault selection method to accelerate the computation time in seed ordering and selection processes. This selection method can be used to restrict faults for test generation executed in an early stage in seed ordering and selection processes, and reduce a test pattern count and therefore a computation time. We evaluate the impact of the selection method both in deterministic BIST, where one test pattern is decoded from one seed, and mixed-mode BIST, where one seed is expanded to two or more patterns. The statistical delay quality level (SDQL) is adopted as test quality measure, to represent ability to detect small delay defects (SDDs). Experimental results show that our proposed method can significantly reduce computation time from 28% to 63% and base set seed counts from 21% to 67% while slightly sacrificing test quality

Analysis and Test of the Effects of Single Event Upsets Affecting the Configuration Memory of SRAM-based FPGAs

SRAM-based FPGAs are increasingly relevant in a growing number of safety-critical application fields, ranging from automotive to aerospace. These application fields are characterized by a harsh radiation environment that can cause the occurrence of Single Event Upsets (SEUs) in digital devices. These faults have particularly adverse effects on SRAM-based FPGA systems because not only can they temporarily affect the behaviour of the system by changing the contents of flip-flops or memories, but they can also permanently change the functionality implemented by the system itself, by changing the content of the configuration memory. Designing safety-critical applications requires accurate methodologies to evaluate the system’s sensitivity to SEUs as early as possible during the design process. Moreover it is necessary to detect the occurrence of SEUs during the system life-time. To this purpose test patterns should be generated during the design process, and then applied to the inputs of the system during its operation. In this thesis we propose a set of software tools that could be used by designers of SRAM-based FPGA safety-critical applications to assess the sensitivity to SEUs of the system and to generate test patterns for in-service testing. The main feature of these tools is that they implement a model of SEUs affecting the configuration bits controlling the logic and routing resources of an FPGA device that has been demonstrated to be much more accurate than the classical stuck-at and open/short models, that are commonly used in the analysis of faults in digital devices. By keeping this accurate fault model into account, the proposed tools are more accurate than similar academic and commercial tools today available for the analysis of faults in digital circuits, that do not take into account the features of the FPGA technology.. In particular three tools have been designed and developed: (i) ASSESS: Accurate Simulator of SEuS affecting the configuration memory of SRAM-based FPGAs, a simulator of SEUs affecting the configuration memory of an SRAM-based FPGA system for the early assessment of the sensitivity to SEUs; (ii) UA2TPG: Untestability Analyzer and Automatic Test Pattern Generator for SEUs Affecting the Configuration Memory of SRAM-based FPGAs, a static analysis tool for the identification of the untestable SEUs and for the automatic generation of test patterns for in-service testing of the 100% of the testable SEUs; and (iii) GABES: Genetic Algorithm Based Environment for SEU Testing in SRAM-FPGAs, a Genetic Algorithm-based Environment for the generation of an optimized set of test patterns for in-service testing of SEUs. The proposed tools have been applied to some circuits from the ITC’99 benchmark. The results obtained from these experiments have been compared with results obtained by similar experiments in which we considered the stuck-at fault model, instead of the more accurate model for SEUs. From the comparison of these experiments we have been able to verify that the proposed software tools are actually more accurate than similar tools today available. In particular the comparison between results obtained using ASSESS with those obtained by fault injection has shown that the proposed fault simulator has an average error of 0:1% and a maximum error of 0:5%, while using a stuck-at fault simulator the average error with respect of the fault injection experiment has been 15:1% with a maximum error of 56:2%. Similarly the comparison between the results obtained using UA2TPG for the accurate SEU model, with the results obtained for stuck-at faults has shown an average difference of untestability of 7:9% with a maximum of 37:4%. Finally the comparison between fault coverages obtained by test patterns generated for the accurate model of SEUs and the fault coverages obtained by test pattern designed for stuck-at faults, shows that the former detect the 100% of the testable faults, while the latter reach an average fault coverage of 78:9%, with a minimum of 54% and a maximum of 93:16%

Transactions on CAD 1578 Optimized Reseeding by Seed Ordering and Encoding

Abstract — Mixed mode Logic BIST applies both pseudorandom test patterns and deterministic test patterns (from an ATPG tool) to the combinational portion of the circuit under test. Each scan test cycle consists of (1) shifting a test pattern into the scan chains, (2) capturing the response to that pattern and (3) shifting the captured response out of the scan chains. The shifting of the test pattern out of the scan chains is overlapped with shifting in the next test pattern. The pattern shifted into the scan chains comes from the output of the PRPG (pseudo random pattern generator); this pattern is determined by the initial state or seed of the PRPG (contents of the PRPG at the beginning of the shifting operation). In a pseudorandom cycle, the initial state is the final state (last PRPG contents) from the previous cycle. The initial state of a deterministic cycle is shifted into the PRPG either from an ATE or from an on-chip BIST controller. This paper describes techniques to minimize the number of deterministic seeds that must be used: the number of seeds determines the required storage either on the ATE or the chip being tested. These techniques interleave pseudorandom and deterministic cycles rather than first applying all of the pseudorandom cycles and then the deterministic cycles. The decision of when to change from a pseudorandom cycle to a deterministic cycle is made by comparing the final state of the pseudorandom cycle with previously generated ATPG patterns or by carrying out fault simulation on the final state. Which deterministic pattern is chosen for the deterministic cycle influences critically the remainder of the test. A methodology for doing this is described. In addition to interleaving test cycles it is possible to use partial cycles in which the PRPG operates for a few clocks without loading the scan chains. This allows a new seed to be present without loading the seed from the ATE or controller. As might be suspected this reduces the number of stored seeds at the penalty of more complexity in the control sequence. These techniques were simulated and compared with conventional reseeding for some ISCAS 89 benchmarks. Improvements varied between 25 % and 85 % in the required seed storage