We analyze all signals of a combinational circuit simultaneously for redundancy. The state of a signal is represented by two binary variables. The first variable is the logic value of the signal. The second variable is the observability status of the signal with respect to all primary outputs. Boolean equations specify local relationships of these variables in a manner similar to the neural network or Boolean satisfiability method. All pairwise terms appearing in these Boolean equations are used to construct an implication graph, for which the transitive closure graph is obtained. Any signal assignments or relations found from the transitive closure are substituted into higher-order terms of the Boolean equations, some of which reduce to pairwise terms. Such cases are iteratively included in the transitive closure until no more reductions are possible. In the final transitive closure, all signals are examined for the following conditions of redundancy: (1) If a signal and its complement imply each other (contradiction) then both stuck-at faults on that signal are redundant; (2) If one value implies the other value (fixation) then one of the stuck-at faults on that signal is redundant; (3) If the true observability status of a signal implies its own false observability status, then both stuck-at faults of that signal are redundant; (4) If a certain value of a signal implies the false observability status, then the corresponding stuck-at fault is redundant. Despite the apparent similarities with the transitive closure based ATPG, the present method is quite different. Here transitive closure is computed just once, and not recomputed or updated separately for each fault as required in ATPG. We give ISCAS '85 benchmark results. For c6288, we could identify 31 out of 33 redu...

Abstract – Implication graphs are used to solve the test generation, redundancy identification, synthesis, and verification problems of digital circuits. We propose a new “oring ” node structure to represent partial implications in a graph. The oring node is the contrapositive of the previously used “anding ” node. An n-input gate requires one oring and one anding nodes to represent all partial implications. This implication graph is shown to be more complete and more compact compared to the previously published (n+1) anding node graph. Introduction of the new oring node finds more redundancies using the transitive closure method. The second contribution of the present work is a set of new algorithms to update transitive closure for every newly added edge in the implication

Abstract – Implication graphs are used to solve the test generation, redundancy identification, synthesis, and verification problems of digital circuits. We propose a new “oring ” node structure to represent partial implications in a graph. The oring node is the contrapositive of the previously used “anding ” node. The addition of a single oring node in the implication graph of a Boolean gate eliminates the need for several anding nodes. An n-input gate requires one oring and one anding nodes to represent all partial implications. This implication graph is shown to be more complete and more compact compared to the previously published (n+1) anding nodes graph. Introduction of the new oring node finds more redundancies using the transitive closure method. The second contribution of the present work is new algorithms

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