High Repair-Efficiency BISR Scheme for RAMs by Reusing Bitmap for Bit Redundancy

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.

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 has been considered as the most cost-effective solution for enhancing yield and reliability of NAND flash memory. Owing to the inherent architecture of NAND flash memory, conventional BISR techniques use spare columns and NAND blocks as the basic replacement elements. These techniques can be categorized as the coarse-grained BISR techniques (CGBISR). It is evident that the efficiency of spare usage is very low. To cure this dilemma, fine-grained BISR (FGBISR) techniques are proposed in this paper. We first exploit the fault behaviors at the circuit level and derive novel and concise repairable fault types (RFTs) for the widely used flash memory fault models. The proposed RFTs include bit-, page-, column-, and NAND block-repairable faults. Therefore, FGBISR can conduct repairing at the fine-grained levels for improving repair efficiency. We also provide efficient redundancy analysis algorithms suitable for VLSI implementation based on the RFTs. The corresponding FGBISR architectures and repair flow are also proposed. A simulator was developed for evaluating repair rate, yield, reliability, and hardware overhead. Experimental results show that repair rate, yield, and reliability can be raised significantly with negligible hardware overhead.

In system on chip (SOC) design, memory occupies a large area, if any defects in the memory that will affect the SOC’s total yield. To avoid this, spare rows and columns are added to the memory. When the fault cells need to be operated, spares rows/columns will be used instead of faulty cells using Built-in self-repair (BISR) logic. Repairing logic includes either row repair/column repair or a combination of both. In this paper, we propose two methods, one is on the memory built-in self-test (BIST) for checking the memory array circuit. In the second method, an optimized BISR is employed to repair the faulty memory cells based upon the Built-in redundancy analysis. In the BIST method, our proposed method injects the faults into memory by using two fault injection techniques called saboteurs and mutants with optimized test pattern logic. After the injection of the faults in the memory cell, the fault memory is repaired with the proposed counting threshold algorithm for the BISR scheme. Simulation and synthesis results are obtained using Mentor Graphics and Xilinx ISE Design Suite. From our obtained results it can be inferred that various performance measures like power, area, and timing details are reduced in the proposed methods when compared with the existing exhaustive methods. And this proves that the performance of the overall process is found to be enhanced. Thereby yield can also be enhanced with minimum time to market.

With the introduction of the drowsy cache design technique (Kim et al., 2004), new fault behaviors appear and more restrictive design rules must be applied to the chip fabrication process. In this research, we simulate all possible spot defects (SDs) under normal mode and drowsy mode in different resistance regions using HSpice. Six new fault models appear with the introduction of drowsy mode for memory arrays. When we derive a march algorithm for the new fault models of this low-power cache, several simplification rules are utilized to reduce the test complexity. As a result, we develop a march algorithm which can detect all SDs in either data caches or instruction caches. Since some faults occur only in drowsy mode, a built-in self-repair (BISR) scheme is developed. By utilizing BISR, the cache can still work even if some cache lines fail to work in drowsy mode

With the nanometer-scale semiconductor technology, built-in self-repair (BISR) schemes are emerging techniques for improving the yield of embedded memories. A built-in self-repairable memory system typically consists of repairable memory cores, wrappers, built-in self-test (BIST) circuit, fuse group, and built-in redundancy-analyzer. This paper presents a system-level verification methodology for built-in self-repairable memory systems. The proposed verification methodology can verify the connectivity between the wrappers and self-repairable memories in a self-repairable memory system. Also, it can verify the wrapper misplaced design errors.

Three-dimensional (3D) memory products based on through silicon via (TSV) are widely developed to fulfill the ever-increasing demands of per unit area storage capacity. The yield is still one of the critical challenges for 3D memory. Redundancy technique is now widely used in industry to improve yield. How to reduce the overhead of redundancy by improving the utilization of redundancy is important to 3D memory. In this paper, we propose a row/column block-based mapping technique for 3D memory built-in self-repair scheme to improve the utilization of redundancy and low hardware overhead. Each row/column is divided into row/column block and the mapping can be performed at row/column-block level instead of the original row/column level. Therefore, more faulty cells can be clustered into the same row/column. Based on the proposed technology, a 3D-essential spare pivoting (ESP) algorithm is also proposed for the allocation of redundant rows and columns, and the area overhead of this algorithm is particularly low. The experimental results show that on an average the repair ratio of our proposed scheme is much better than the fault clustering technique by 12% and the redundancy-cost can reduce 23%.

Reconfigurable memory arrays with spare rows and columns are quite frequently used as reliable data storage components in present age System-on-Chips (SoCs). The spare memory rows and columns can be utilized to automatically replace rows or columns that are found to contain a cell fault after fabrication. One of the biggest SoC design challenges is to estimate, prior to the actual fabrication process, the right number of these spare rows and spare columns for meeting the reliability specifications. Traditionally, computer simulation techniques are used to perform probabilistic analysis of reconfigurable memory arrays but they provide inaccurate results. To ensure accurate analysis and thus more reliable SoC designs, we propose, in this paper, a probabilistic theorem proving approach in the domain of reconfigurable memory array analysis. We present a higher-order-logic stuck-at fault model for reconfigurable memory arrays, based on which, we illustrate the formal verification of some key statistical properties related to the number of stuck-at faults and the repairability condition.

In this paper, we proposed an approach to reduce the hardware required by the conventional algorithm CRESTA (Comprehensive Real-time Exhaustive Search Test and Analysis) by trying to share the hardware between different sub-analyzers. Experiments with synthesis for the case of three spare rows and three spare columns evidenced for saving at least 15% of hardware, and for the case of two spare rows and two spare columns — about 7%.

To increase device memory yield, many manufacturers use incorporated redundancy to replace faulty cells. In this redundancy technology, the implementation of an effective redundancy analysis (RA) algorithm is essential. Various RA algorithms have been developed to repair faults in memory. However, nearly all of these RA algorithms have low analysis speeds. The more densely compacted the memory is, the more testing and repair time is needed. Even if the analysis speed is very high, the RA algorithm would be useless if it did not have a normalized repair rate of 100%. In addition, when the number of added spares is increased in the memory, then the memory space that must be searched with the RA algorithms can exceed the memory space within the automatic test equipment. A very efficient RA algorithm using simple calculations is proposed in this work so as to minimize both the repair time and memory consumption. In addition, the proposed algorithm generates an optimal solution using a tree-based algorithm in each fault group. Our experiment results show that the proposed RA algorithm is very efficient in terms of speed and repair.

An efficient on-chip infrastructure for memory test and repair is crucial to enhance yield and availability of SoCs. A commonly used repair strategy is to equip memories with spare rows and columns (2D redundancy). To avoid the prohibitive storage requirements for failure bitmaps and the complex data structures inherent in most algorithms for offline repair analysis, existing heuristics for built-in repair analysis (BIRA) either use very simple search strategies or restrict the search to smaller local bitmaps. Exact BIRA algorithms work with sub analyzers for each possible repair combination. While a parallel implementation suffers from a high hardware overhead, a serial implementation leads to increased test times. Recently an integrated built-in test and repair approach has been proposed which interleaves test and repair analysis and supports an exact solution with moderate hardware overhead and reasonable test times. The search is based on a depth first traversal of a binary tree, which can be efficiently implemented using a stack of limited size. This algorithm can be realized with different repair strategies guiding the selection of spare rows or columns in each step. In this paper the impact of four different repair strategies on the test and repair time is analyzed.

Yield enhancement techniques for 3-dimensional random access memories

Repairable embedded memories help improve the overall yield of an IC. We have developed a yield analysis tool that provides realistic yield estimates for both single repairable memories, as well as for ICs containing multiple, possibly different, repairable embedded memories. Our approach uses pseudo-randomly generated fault bit-maps, which are based on memory area size, defect density, and fault distribution. In order to accommodate a wide range of industrial memory and redundancy organizations, we have developed a flexible memory model. It generalizes the traditional simple memory matrix model with partitioning into regions, grouping of columns and rows, and column-wise and row-wise coupling of the spares. Our tool is used to determine an optimal amount of spare columns and rows for a given memory, as well as to determine the effectiveness of various repair algorithms.

The test cost and yield improvement of embedded memories have become very important as memory capacity and density have grown. For embedded memories, built-in redundancy analysis (BIRA) is widely used to improve yield by replacing faulty cells with a 2-D redundancy architecture. However, the most important factor in BIRA is the reduction of hardware overhead while keeping optimal repair rate. Most BIRA approaches require extra hardware overhead in order to store and analyze faults in the memory. These approaches do not utilize spare memories during the redundancy analysis (RA) procedure. However, the proposed BIRA minimizes area overhead by utilizing a part of the spare memory as an address mapping table (AMT). Since storing the faulty memory addresses take most of the extra hardware overhead, the reduced logical addresses produced by the AMT are used to reduce the extra hardware overhead. In addition, the reduced addresses are stored in content-addressable memories (CAMs) and used in the RA procedure. The proposed BIRA can achieve an optimal repair rate by using an exhaustive search RA algorithm. The proposed RA algorithm compares the repair solution candidates with all the fault addresses stored in the proposed CAMs to guarantee an exhaustive search. The experimental results show that the proposed BIRA requires a smaller area overhead than that of the previous state-of-the-art BIRA with an optimal repair rate.

최근의 시스템 온 칩 (SoC) 설계 기술의 발전에 따라, 수백개의 임베디드 메모리 코어들이 칩의 대부분의 면적을 차지하고 있다. 그러므로 시스템 온 칩의 수율은 임베디드 메모리 코어들의 수율에 따라 결정된다고 볼 수 있다. 최적의 수리 효율을 가지는 built-in self repair (BISR)을 모든 메모리들이 가지고 있게 된다면 면적의 부담이 너무 크다. 본 논문에서는 이와 같은 면적의 부담을 줄이기 위하여 메모리들을 그룹화 한 후에 비트맵 메모리를 공유하여 면적 부담을 크게 줄이는 방법을 제안한다. 제안하는 비트맵 메모리 공유방법은 built-in redundancy analysis (BIRA)의 면적을 크게 줄일 수 있다. 실험결과를 통해서 보면 제안하는 방법이 면적 부담을 대략 80%정도 줄이는 것을 확인 할 수 있다.