Temperature Aware and Defect-Probability Driven Test Scheduling for System-on-Chip

The high complexity of modern electronic systems has resulted in a substantial increase in the time-to-market as well as in the cost of design, production, and testing. Recently, in order to reduce the design cost, many electronic systems have employed a core-based system-on-chip (SoC) implementation technique, which integrates pre-defined and pre-verified intellectual property cores into a single silicon die. Accordingly, the testing of manufactured SoCs adopts a modular approach in which test patterns are generated for individual cores and are applied to the corresponding cores separately. Among many techniques that reduce the cost of modular SoC testing, test scheduling is widely adopted to reduce the test application time. This thesis addresses the problem of minimizing the test application time for modular SoC tests with considerations on three critical issues: high testing temperature, temperature-dependent failures, and defect probabilities. High temperature occurs in testing modern SoCs and it may cause damages to the cores under test. We address the temperature-aware test scheduling problem aiming to minimize the test application time and to avoid the temperature of the cores under test exceeding a certain limit. We have developed a test set partitioning and interleaving technique and a set of test scheduling algorithms to solve the addressed problem. Complicated temperature dependences and defect-induced parametric failures are more and more visible in SoCs manufactured with nanometer technology. In order to detect the temperature-dependent defects, a chip should be tested at different temperature levels. We address the SoC multi-temperature testing issue where tests are applied to a core only when the temperature of that core is within a given temperature interval. We have developed test scheduling algorithms for multi-temperature testing of SoCs. Volume production tests often employ an abort-on-first-fail (AOFF) approach which terminates the chip test as soon as the first fault is detected. Defect probabilities of individual cores in SoCs can be used to compute the expected test application time of modular SoC tests using the AOFF approach. We address the defect-probability driven SoC test scheduling problem aiming to minimize the expected test application time with a power constraint. We have proposed techniques which utilize the defect probability to generate efficient test schedules. Extensive experiments based on benchmark designs have been performed to demonstrate the efficiency and applicability of the developed techniques