Accelerated dynamic learning for test pattern generation in combinational circuits

An efficient technique for dynamic learning called oriented dynamic learning is proposed. Instead of learning being performed for almost all signals in the circuit, it is shown that it is possible to determine a subset of these signals to which all learning operations can be restricted. It is further shown that learning for this set of signals provides the same knowledge about the nonsolution areas in the decision trees as the dynamic learning of SOCRATES. High efficiency is achieved by limiting learning to certain learning lines that lie within a certain area of the circuit, called the active area. Experimental results are presented to show that oriented dynamic learning is far more efficient than dynamic learning in SOCRATES.<
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