Title :
A Built-In Repair Analyzer With Optimal Repair Rate for Word-Oriented Memories
Author :
Jaeyong Chung ; Joonsung Park ; Abraham, J.A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Texas, Austin, TX, USA
Abstract :
This paper presents a built-in self repair analyzer with the optimal repair rate for memory arrays with redundancy. The proposed method requires only a single test, even in the worst case. By performing the must-repair analysis on the fly during the test, it selectively stores fault addresses, and the final analysis to find a solution is performed on the stored fault addresses. To enumerate all possible solutions, existing techniques use depth first search using a stack and a finite-state machine. Instead, we propose a new algorithm and its combinational circuit implementation. Since our formulation for the circuit allows us to use the parallel prefix algorithm, it can be configured in various ways to meet area and test time requirements. The total area of our infrastructure is dominated by the number of content addressable memory entries to store the fault addresses, and it only grows quadratically with respect to the number of repair elements. The infrastructure is also extended to support various types of word-oriented memories.
Keywords :
built-in self test; combinational circuits; content-addressable storage; finite state machines; redundancy; built-in self repair analyzer; combinational circuit; content addressable memory; depth first search; finite-state machine; memory arrays; must-repair analysis; optimal repair rate; parallel prefix algorithm; redundancy; single test; stack machine; stored fault addresses; word-oriented memories; Built-in self-test; Circuit faults; Computer aided manufacturing; Maintenance engineering; Registers; Resource management; System-on-a-chip; Built-in self repair (BISR); memory test; redundancy allocation; repair analysis; spare allocation;
Journal_Title :
Very Large Scale Integration (VLSI) Systems, IEEE Transactions on
DOI :
10.1109/TVLSI.2011.2182217