X-Align: Improving the Scan Cell Observability of Response Compactors

Abstract

While response compaction reduces the size of expected vectors that need to be stored on tester memory, the consequent information loss inevitably reflects into loss in test quality. Unknown x's further exacerbate the quality loss problem, as they mask out errors captured in other scan cells in the presence of response compactors. In this paper, we propose a technique that manipulates the x distribution in scan responses prior to their propagation into the response compactor. A block, which we refer to as x-align, inserted between the scan chains and the response compactor aligns response x's within the same slices as much as possible in order to increase the number of scan cells that can be observed through the compactor. The alignment of x's is achieved by delaying the scan-out operations in the scan chains, wherein the proper delay values are computed judiciously. We present an Integer Linear Programming (ILP) formulation and a computationally efficient greedy heuristic for the computation of the delay values for scan chains. The x-align hardware is generic yet reconfigurable. An analysis of x distribution in a captured response helps compute the proper delay values, with which x-align is reconfigured to maximize the alignment of x's. The scan cell observability enhancement delivered by x-align paves the way for the utilization of simple response compactors, such as parity trees, yet providing high levels of test quality even in the presence of a large density of response x's. X-align can also be utilized with any response compactor to manipulate the x distribution in favor of the compactor, thus improving the test quality attained.