Testing and diagnosis techniques play a key role in the advance of semiconductor memory technology. The challenge of failure detection has created intensive investigation on efficient testing and diagnosis algorithm for better fault coverage and diagnostic resolution. At present, March test algorithm is used to detect and diagnose all faults related to Random Access Memories. However, the test and diagnosis process are mainly done manually. Due to this, a systematic approach for developing and evaluating memory test algorithm is required. This work is focused on incorporating the March based test algorithm using a software simulator tool for implementing a fast and systematic memory testing algorithm. The simulator allows a user through a GUI to select a March based test algorithm depending on the desired fault coverage and diagnostic resolution. Experimental results show that using the simulator for testing is more efficient than that of the traditional testing algorithm. This new simulator makes it possible for a detailed list of stuck-at faults, transition faults and coupling faults covered by each algorithm and its percentage to be displayed after a set of test algorithms has been chosen. The percentage of diagnostic resolution is also displayed. This proves that the simulator reduces the trade-off between test time, fault coverage and diagnostic resolution. Moreover, the chosen algorithm can be applied to incorporate with memory built-in self-test and diagnosis, to have a better fault coverage and diagnostic resolution. Universities and industry involved in memory Built-in-Self test, Built-in-Self repair and Built-in-Self diagnose will benefit by saving a few years on researching an efficient algorithm to be implemented in their designs.

This paper presents an efficient diagnosis scheme for RAMs. Three March-based algorithms are proposed to diagnose simple functional faults of RAMs. A March-15N algorithm is used for locating and partially diagnosing faults of bit-oriented or word-oriented memories, where N represents the address number. Then a 3N March-like algorithm is used for locating the aggressor words (bits) of coupling faults (CFs) in word-oriented (bit-oriented) memories. It also can distinguish the faults which cannot be identified by the March-15N algorithm. Thus, the proposed diagnosis scheme can achieve full diagnosis and locate aggressors with (15N + 3mN) Read/Write operations for a bit-oriented RAM with m CFs. For word-oriented RAMs, a March-like algorithm is also proposed to locate the aggressor bit in the aggressor word with 4 log2B Read/Write operations, where B is the word width. Analysis results show that the proposed diagnosis scheme has higher diagnostic resolution and lower time complexity than the previous fault location and fault diagnosis approaches. A programmable built-in self-diagnosis (BISD) design is also implemented to perform the proposed diagnosis algorithms. Experimental results show that the area overhead of the BISD is small--only about 2.17% and 0.42% for 16 K8-bit and 16 K128-bit SRAMs, respectively.

Most of system-on-chips (SOCs) have many memory cores. Diagnosis is often used to improve the yield of memories. Memory cores usually represent a significant portion of the chip area and dominate the yield of the chip. Memory diagnosis thus is one of key techniques for improving the yield and quality of SOCs. Content addressable memories (CAMs) are important components in many SOCs. In this paper we propose a three-phase diagnosis procedure for binary CAMs (BCAMs). The user can distinguish different types of BCAM-specific comparison and RAM faults and locate the faulty cells with the procedure. A March-like fault identification algorithm is also proposed. The algorithm can distinguish different types of faults--including typical RAM faults and BCAM-specific comparison faults. The algorithm requires 15N Read/Write operations and 2(N + B) Compare operations for an N B-bit BCAM. Analysis results show that the algorithm has 100% diagnostic resolution for the target faults.