Built-in self-repair (BISR) techniques are widely used for the repair of embedded memories. One of the key components of a BISR circuit is the built-in redundancy-analysis (BIRA) module, which allocates redundancies according to the designed redundancy analysis algorithm. Thus, the BIRA module affects the repair rate of the BISR circuit. Existing BIRA schemes for RAMs can provide the optimal repair rate (the ratio of the number of repaired RAMs to the number of defective RAMs), but they require either high area cost or multiple test runs. This paper proposes a BIRA scheme for RAMs, which can provide the optimal repair rate using very low area cost and single test run. Furthermore, the BIRA is designed as reconfigurable such that it can be shared by multiple RAMs. Experimental results show that the area cost for implementing the proposed BIRA scheme is much lower than that of existing BIRA schemes with optimal repair rate. A test chip is also implemented to demonstrate the proposed BIRA scheme.

Built-in self-repair (BISR) techniques are widely used to enhance the yield of random access memories (RAMs) in a system-on-chip (SoC) which typically consists of hundreds of RAMs. Hence, many BISR circuits may be needed in a such SoC. Effective techniques for planning these BISR circuits thus are imperative. In this paper, we propose a memory BISR planning (MBiP) framework for the RAMs in SoCs. The MBiP framework consists of a memory grouping algorithm for selecting RAMs which can share a BISR circuit. Then, a test scheduling algorithm is used to determine the test sequence of RAMs in a SoC under the constraint of test power. Finally, a BISR scheme allocation algorithm is proposed to allocate different BISR schemes for the RAMs under the constraints of the results of memory grouping and test scheduling. Simulation results show that the proposed MBiP can effectively plan the BISR schemes for the RAMs in a SoC. For example, about 22% area reduction can be achieved by the BISR schemes planned by the proposed MBiP framework for 50 RAMs under 1.5 mm distance constraint and 350 mW test power constraint in comparison with a dedicated BISR scheme (i.e., each RAM has a self-contained BISR circuit).

Built-in self-repair (BISR) techniques are widely used for repairing embedded random access memories (RAMs). One key component of a BISR module is the built-in redundancy-analysis (BIRA) design. This paper presents an effective BIRA scheme which executes the 2-D redundancy allocation based on a 1-D local bitmap. Two BIRA algorithms for supporting two different redundancy organizations are also proposed. Simulation results show that the proposed BIRA scheme can provide high repair rate (i.e., the ratio of the number of repaired memories to the number of defective memories) for the RAMs with different fault distributions. Experimental results show that the hardware overhead of the BIRA design is only about 2.9% for an 8192 × 64-bit RAM with two spare rows and two spare columns. Also, the ratio of the BIRA analysis time to the test time is only about 0.02% if the March-CW test is performed. Furthermore, a simulation flow is proposed to determine the size of the 1-D local bitmap such that the BIRA algorithm can provide the best repair rate using the smallest-size 1-D local bitmap.

Built-in self-repair (BISR) techniques are widely used to enhance the yield of memories in a system-on-chip (SOC). A SOC typically consists of hundreds of memories. Cost-efficient BISR schemes for repairing those memories thus are imperative. In this paper, we propose a memory BISR automatic generation (MBAG) framework for designing memory BISR circuits in a SOC. The MBAG framework consists of a test scheduling engine and a memory grouping engine for the minimization of test time and area cost of the BISR circuits. The test scheduling algorithm has been presented in our previous work [1]. In this paper, therefore, we focus on the introduction of the grouping algorithm determining the memories which can share a BISR circuit under the constraints of distance and scheduling results. Simulation results show that the proposed MBAG can generate reconfigurable BISR circuits for 20 memories such that 50% area reduction is achieved in comparison with a dedicated BISR scheme if the distance constraint is 3mm and the test power constraint is 80mW.

Built-in self-repair (BISR) techniques are widely used to enhance the yield of embedded random access memories (RAMs). Fault-location ability of test algorithms executed by a BISR circuit has heavy impact on the repair efficiency of the BISR circuit. This paper proposes a defect-aware BISR (DABISR) scheme for single-port RAMs (SPRAMs) and multi-port RAMs (MPRAMs) in system chips. Multiple RAMs can share a DABISR such that the area cost of DABISR is drastically reduced. We also present two defect-location algorithms (DLAs) for identification of bridge defects between word-lines and bit-lines of MPRAMs. The DABISR can perform DLAs to locate bridge defects such that it can provide high repair efficiency. For example, simulation results show that if a faulty two-port RAM has 20% inter-port faults, the DLAs can help to gain 8.4-14.4% increase of repair rate for different redundancy configurations. In comparison with an existing shared BISR scheme, however, the DABISR only incurs about 0.34% additional area overhead to support the function of DLAs.

This paper proposes a simple method for enhancing the reliability of static random access memories (SRAMs) with hard-to-detect resistive-open defects. The method prevents a SRAM from executing successive multiple read operations on the same position, such that the hard-to-detect defects cannot manifest as functional faults. This can prolong the lifetime of the SRAM with latent hard-to-detect defects. Experimental results show that the proposed reliability-enhancement circuit (REC) can effectively improve the reliability of the SRAMs without incurring delay penalty and with 0.07% additional area cost for an 8192 × 64-bit SRAM. By integrating the REC with the SRAM, a BISR scheme is proposed to boost 6%-10% increment of repair rate compared with the BISR without the REC. Also, the area cost of the BISR is low-only about 2% for an 8192 × 64-bit SRAM.

Built-in self-repair (BISR) technique has been widely used to repair embedded random access memories (RAMs). This paper presents a reconfigurable BISR (ReBISR) scheme for repairing RAMs with different sizes and redundancy organizations. An efficient redundancy analysis algorithm is proposed to allocate redundancies of defective RAMs. In the ReBISR, a reconfigurable built-in redundancy analysis (ReBIRA) circuit is designed to perform the redundancy algorithm for various RAMs. Also, an adaptively reconfigurable fusing methodology is proposed to reduce the repair setup time when the RAMs are operated in normal mode. Experimental results show that the ReBISR scheme can achieve high repair rate (i.e., the ratio of the number of repaired RAMs to the number of defective RAMs). The area cost of the ReBISR is very small, which is only about 2.7% for four RAMs (one 4 Kbit RAM, one 16 Kbit RAM, one 128 Kbit RAM, and one 512 Kbit RAM). Moreover, the time overhead of redundancy analysis is very small. For example, the ratio of the redundancy analysis time to the test time for a 512 Kbit RAM tested by a March-14 test with solid data backgrounds is only about 0.25%. On the other hand, the proposed fusing scheme can achieve about 86.94% reduction of repair setup time in comparison with a typical fusing scheme for 20 512 × 16 × 64-bit RAMs of which each RAM has one spare row and one spare column.

Embedded memories currently constitute a significant portion of the chip area for typical system-on-chip (SOC) designs. Built-in self-repair (BISR) techniques have been widely used for enhancing the yield of embedded memories. This paper proposes a shared parallel BISR scheme for random access memories (RAMs) in SOCs. The shared parallel BISR can test and repair multiple RAMs simultaneously. A global time-multiplexed built-in redundancy analyzer (TM-BIRA) is used to allocate redundancies of the RAMs under test and repair. We also design a 1500-compatible wrapper for chip-level control of the shared parallel BISR circuits. In comparison with the dedicated parallel BISR scheme (each memory has a self-contained BISR circuit), the proposed parallel BISR scheme can achieve 20% reduction of area cost by paying additional 0.005% test and repair time for serving 5 RAMs with spare rows and spare columns.

Built-in self-repair (BISR) techniques have been widely used for enhancing the yield of embedded memories. This paper presents an efficient BISR scheme for multiport RAMs (MPRAMs). The BISR scheme has a defect-location module (DLM) executing a defect-location algorithm to locate inter-port defects. This enhances the fault-location capability of the applied test algorithm with only a few amount of cost of testing time. A built-in redundancy analyzer (BIRA) executing a proposed redundancy analysis algorithm is also proposed to allocate two-dimension redundancy of MPRAMs. Experimental results show that if a faulty MPRAM has 20% inter-port faults, the DLM can boost the increment of repair rate from 8.4% to 14.4% for different redundancy configurations. The area cost of the BIRA and DLM is small, it is only about 1% for a 4096 times 128-bit MPRAM with 1 spare row and 1 spare IO.

Complex system-on-a-chip (SOC) designs usually consist of many memory cores. Efficient yield-enhancement techniques thus are required for the memory cores in SOCs. This paper presents an infrastructure intelligent property (IIP) for testing, diagnosing, and repairing multiple memory cores in SOCs. The proposed IIP can perform parallel testing for multiple memories, and serial diagnosis or repair for one memory each time. In the repair mode, the proposed IIP can execute various redundancy analysis algorithms. Therefore, the user can select a better redundancy analysis algorithm for each memory core being tested according to its redundancy structure. Simulation results show that the proposed IIP needs less test time and redundancy analysis time than the processor-based built-in self-repair scheme. We also have realized the proposed IIP for four types of memories - two 8 K 64 bit SRAMs, one 4 K x 16 bit SRAM, and one 2 K x 32 bit SRAM - based on TSMC 0.18-mum standard cell technology. Simulation results show that the area overhead of the IIP is only about 4.6%.

Built-in self-repair (BISR) technique is gaining popular for repairing embedded memory cores in system-on-chips (SOCs). To increase the utilization of memory redundancy, the BISR technique usually needs to perform built-in redundancy-analysis (BIRA) algorithm for redundancy allocation. This paper presents an efficient BIRA scheme for embedded memory repair. The BIRA scheme executes the 2D redundancy allocation based on the ID local bitmap. This enables that the BIRA circuitry can be implemented with low area cost. Also, the BIRA algorithm can provide good repair rate (i.e., the ratio of the number of repaired memories to the number of defective memories). Experimental results show that the repair rate of the proposed BIRA scheme approximates to that of the optimal scheme for the memories with different fault distributions. Also, the ratio of the analysis time to the test time is small.

This paper presents a reconfigurable built-in self-repair (ReBISR) scheme for multiple repairable RAM cores with different sizes and redundancy organizations (i.e., spare rows/spare columns or spare rows/spare IOs). We also propose an efficient built-in redundancy-analysis (BIRA) algorithm for allocating redundancies for the ReBISR scheme. A reconfigurable BIRA (ReBIRA) circuit is realized to perform the proposed BIRA algorithm for the ReBISR scheme. Experimental results show that the ReBISR scheme can achieve high repair rate (i.e., the ratio of the number of repaired memories to the number of defective memories). The area cost of the reconfigurable BIRA is very small, e.g., the area cost is only about 1.5% if 512times4times256 design parameters and four memory instances (64times2times32, 128times2times64, 256times4times128, and 512times4times256) are considered. Also, the ratio of the redundancy analysis time to the test time is very small, e.g., the ratio for a 512times4times256-bit memory tested by a March-14N algorithm with solid data backgrounds is only about 0.25%

Memory cores are usually the densest portion with the smallest feature size in system-on-chip (SOC) designs. The reliability of memory cores thus has a heavy impact on the reliability of SOCs. The transparent test is a useful technique for improving the reliability of memories during their life time. The paper presents a systematic algorithm used for transforming a bit-oriented march test into a transparent word-oriented march test. The transformed transparent march test has shorter test complexity compared with those proposed previously (Nicolaidis, M., IEEE Trans. Computers, vol.45, no.10, p.1141-56, 1996; Thaller, K. and Steininger, A., IEEE Trans. Reliability, vol.52, no.4, p.413-22, 2003). For example, if a memory with 32-bit words is tested with March C-, the time complexity of the transparent word-oriented test transformed by the proposed scheme is only about 56% and 19% of the time complexity of the transparent word-oriented test converted by the schemes reported by Nicolaidis and by Thaller and Steininger, respectively.