Cellular Automata-Based Signature Analysis for Built-In Self-Test
IEEE Transactions on Computers
Design Of A Universal BIST (UBIST) Structure
VLSID '03 Proceedings of the 16th International Conference on VLSI Design
Image security system using recursive cellular automata substitution
Pattern Recognition
Accumulator-based pseudo-exhaustive two-pattern generation
Journal of Systems Architecture: the EUROMICRO Journal
Characterization of 1-d Periodic Boundary Reversible CA
Electronic Notes in Theoretical Computer Science (ENTCS)
Behavior of complemented CA whose complement vector is acyclic in a linear TPMACA
Mathematical and Computer Modelling: An International Journal
Cellular automata-based parallel random number generators using FPGAs
International Journal of Reconfigurable Computing
On the combination of self-organized systems to generate pseudo-random numbers
Information Sciences: an International Journal
An effective two-pattern test generator for Arithmetic BIST
Computers and Electrical Engineering
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A variation on a built-in self-test technique is presented that is based on a distributed pseudorandom number generator derived from a one-dimensional cellular automata (CA) array. The cellular automata-logic-block-observation circuits presented are expected to improve upon conventional design for testability circuitry such as built-in logic-block operation as a direct consequence of reduced cross correlation between the bit streams that are used as inputs to the logic unit under test. Certain types of circuit faults are undetectable using the correlated bit streams produced by a conventional linear-feedback-shift-register (LFSR). It is also noted that CA implementations exhibit data compression properties similar to those of the LFSR and that they display locality and topological regularity, which are important attributes for a very large-scale integration implementation. It is noted that some CAs may be able to generate weighted pseudorandom test patterns. It is also possible that some of the analysis of pseudorandom testing may be more directly applicable to CA-based pseudorandom testing than to LFSR-based schemes