A test solution for a complex system requires the design of a test access mechanism (TAM), which is used for the test data transportation, and a test schedule of the test data transportation on the designed TAM. An extensive TAM will lead to lower test-application time at the expense of higher routing costs, compared to a simple TAM with low routing cost but long testing time. It is also possible to reduce the testing time of a testable unit by loading the test vectors in parallel, thus increasing the parallelization of a test. However, such a test-time reduction often leads to higher power consumption, which must be kept under control since exceeding the power budget could damage the system under test. Furthermore, the execution of a test requires resources and concurrent execution of tests may not be possible due to resource or other conflicts. In this paper, we propose an integrated technique for test scheduling, test parallelization, and TAM design, where the test application time and the TAM routing are minimized, while considering test conflicts and power constraints. The main features of our technique are the efficiency in terms of computation time and the flexibility to model the system's test behavior, as well as the support for the testing of interconnections, unwrapped cores and user-defined logic. We have implemented our approach and made several experiments on benchmarks as well as industrial designs in order to demonstrate that our approach produces high-quality solution at low computational cost.

We propose a technique integrating test scheduling, scan chain partitioning and test access mechanism (TAM) design to minimize the test time and the TAM routing cost while considering test conflicts and power constraints. The main features of our technique are (1) the flexibility in modelling the systems test behaviour and (2) the support for interconnection test of unwrapped cores and user-defined logic. Experiments using our implementation on several benchmarks and industrial designs demonstrate that it produces high quality solution at low computational cost.