Process-variation and temperature aware soc test scheduling using particle swarm optimization

High working temperature and process variation are undesirable effects for modern systems-on-chip. It is well recognized that the high temperature should be taken care of during the test process. Since large process variations induce rapid and large temperature deviations, traditional static test schedules are suboptimal in terms of speed and/or thermal-safety. A solution to this problem is to use an adaptive test schedule which addresses the temperature deviations by reacting to them. We propose an adaptive method that consists of a computationally intense offline-phase and a very simple online-phase. In the offline-phase, a near optimal schedule tree is constructed and in the online-phase, based on the temperature sensor readings, an appropriate path in the schedule tree is traversed. In this paper, particle swarm optimization is introduced into the offline-phase and the implications are studied. Experimental results demonstrate the advantage of the proposed method.