Improving test compression by retaining non-pivot free variables in sequential linear decompressors

Abstract

Sequential linear decompressors are inherently efficient and attractive for compressing test cubes with many don't cares. The test cubes are encoded by solving a system of linear equations. In continuous decompression, typically a fixed number of free variables are used to encode each test cube in a “one-size-fits-all” manner. The non-pivot free variables used in Gaussian elimination are wasted when the decompressor is reset before decompressing the next test cube. This paper explores techniques for retaining the non-pivot free variables for a test cube and using them to help encode subsequent test cubes and hence improve encoding efficiency. This approach retains most of the non-pivot free variables with only a minimal increase in runtime for solving the equations and no added control information. Experimental results are presented showing that the encoding efficiency, and hence compression, can be significantly boosted.