Built-in Redundancy Analysis Research Articles

Optimal Method for Test and Repair Memories Using Redundancy Mechanism for SoC.

The current system-on-chip (SoC)-based devices uses embedded memories of enormous size. Most of these systems’ area is dense with memories and promotes different types of faults appearance in memory. The memory faults become a severe issue when they affect the yield of the product. A memory-test and -repair scheme is an attractive solution to tackle this kind of problem. The built-in self-repair (BISR) scheme is a prominent method to handle this issue. The BISR scheme is widely used to repair the defective memories for an SoC-based system. It uses a built-in redundancy analysis (BIRA) circuit to allocate the redundancy when defects appear in the memory. The data are accessed from the redundancy allocation when the faulty memory is operative. Thus, this BIRA scheme affects the area overhead for the BISR circuit when it integrates to the SoC. The spare row and spare column–based BISR method is proposed to receive the optimal repair rate with a low area overhead. It tests the memories for almost all the fault types and repairs the memory by using spare rows and columns. The proposed BISR block’s performance was measured for the optimal repair rate and the area overhead. The area overhead, timing, and repair rate were compared with the other approaches. Furthermore, the study noticed that the repair rate and area overhead would increase by increasing the spare-row/column allocation.

Effective Spare Line Allocation Built-in Redundancy Analysis With Base Common Spare for Yield Improvement of 3D Memory

The memory cell density and memory capacity have been increased for obtaining larger and faster memory. However, this threatens the memory reliability by increasing the probability of faulty cell generation. In this paper, a fast and small-area built-in redundancy analysis (RA) for the post-bond repair process in 3D memory is proposed. Spare line allocation is the structure for memory repair which the most widely used. However, the spare line structure that replaces the entire line which contains faults in each memory layer occurs inefficiency in repairing a small number of faults, including single faults, and it is difficult to share spares between layers. The proposed idea is to use spare line to repair line faults according to the characteristics of it, and to complement the structural limitations by proposing a new redundant spare. This is base common spare located on the base die, and it can be shared across all layers. The proposed BIRA improves the efficiency of spare lines with two complementary spare resource structures, achieving a short repair time and high repair rate. The proposed BIRA achieves 100% repair rate when there are at least 2 times more faults than the previous works with the same hardware overhead. In addition, it takes only at most 80% of the analysis time compared to the previous works, and as the number of faults increases, the analysis time reduction becomes greater compared to the previous works.

Optimized counting threshold Built-in redundancy analysis for memories

Memory plays pivotal roles in any System on chip (SOC) design. While manufacturing the memories, there may be a chance of defects in the memory so it will influence its total yield. So, it is necessary to ensure the defect less memory while manufacturing itself. There are certain techniques like the Built-in self-test (BIST) to test the working condition of the memory in the chip itself. If the memory is repairable then it will not affect the total yield. There are repairing mechanisms such as Built-in redundancy analysis (BIRA), which can be used to repair the memory in the chip itself. In this paper, we propose anoptimized Built-In Redundancy analysis algorithm, which uses Customized Fibonacci Based Test Pattern Generation (CFBTPG) and optimized counting threshold logic for testing and repairing analysis.The proposed method shows better results in comparing with the existing counting threshold algorithm and commonly used Comprehensive Real-time Exhaustive Search Test and Analysis (CRESTA) algorithms.

RETRACTED ARTICLE: A saboteur and mutant based built-in self-test and counting threshold-based built-in self repairing mechanism for memories

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.

Dynamic Built-In Redundancy Analysis for Memory Repair

As advances in memory density and capacity result in an increase in the probability of fault occurrence, many studies on built-in redundancy analysis (BIRA) have been conducted to address this problem. However, conventional BIRAs cannot directly find a final repair solution as soon as test sequences of the built-in self-test (BIST) are over, because they require starting the fault analyses after finishing the test sequences to achieve an optimal repair rate. For this reason, additional analysis time is inevitable, which affects total test costs. In this paper, a dynamic BIRA is proposed for memory repair. It can find a final repair solution directly as soon as test sequences if the BIST are over and achieve an optimal repair rate. The proposed BIRA can restore faults in fault-storing content-addressable memories whenever the spaces in them can be reduced via dynamic fault analysis. Furthermore, the proposed BIRA can be implemented with a reasonable hardware size. This is demonstrated via experiments.

An Efficient BIRA Utilizing Characteristics of Spare Pivot Faults

The current growth of micro-semiconductor technologies requires that an effective solution be found to address the yield and reliability issues associated with embedded memories. A common solution is built-in redundancy analysis (BIRA), which is utilized to guarantee reasonable memory yields. The most common form of BIRA is a module that stores and analyzes fault addresses with a 2-D spare architecture. When the performance of BIRA is evaluated, numerous different parameters are considered, such as repair rate, area overhead, and analysis speed. Because there is a tradeoff between these criteria, many BIRA approaches have been studied so that an ideal BIRA can be found. A novel BIRA approach that focuses on a 100% repair rate and a minimal area overhead is proposed in this paper. In the fault collection phase, the proposed BIRA stores only the essential part of fault addresses in content addressable memories (CAMs), with the rest of the fault addresses being stored in spare memories. After the fault collection phase, a redundancy analysis procedure is performed with the minimum amount of fault information that is stored in the proposed CAM structure. By doing so, the proposed BIRA algorithm can repair all repairable faulty memories while maintaining a minimal area overhead. Our experimental results confirm that the proposed approach exhibits outstanding performance for area overhead, especially when compared to other BIRA approaches that have 100% repair rates.

Fast Built-In Redundancy Analysis Based on Sequential Spare Line Allocation

Built-in redundancy analysis (BIRA) is widely used for memory yield improvement. However, increases in fault occurrence probability inevitably lead to the use of various spare lines to achieve a high repair rate. Generally, it is difficult to apply conventional BIRAs for memories with various spare lines because they focus on a simple spare structure. Therefore, this study examines a BIRA that focuses on a various spare lines structure. The proposed BIRA achieves a high repair rate through the use of various spare lines. Although long analysis time is typically required due to the use of various spare lines, the proposed BIRA solves the problem through sequential spare line allocation. Additionally, it achieves hardware overhead reduction through a simple analyzer. These advantages of the proposed BIRA are demonstrated experimentally.

An Area-Efficient BIRA With 1-D Spare Segments

The growing capacity and density of embedded memories increases the probability of defects and affects the yield. To improve the yield, built-in redundancy analysis (BIRA) has been developed to replace faulty cells with healthy redundant cells. BIRA requires a high repair rate and a feasible hardware size for implementation. Although many BIRAs have been proposed, most of them still demonstrate a low repair rate or a large required hardware size. The proposed BIRA employs an intuitive algorithm with a small-area analyzer that uses 1-D spare segments in the 2-D spare structure. Because most faults in the memory are single faults, spare segments can be used to efficiently allocate redundancies. In terms of the yield, 1-D spare segments are effective when used with an intuitive algorithm that can be implemented with a small hardware overhead. Experimental results show that the proposed BIRA has a higher repair rate and relatively low hardware overhead than state-of-the-art BIRAs and has the advantages of 1-D spare segments.

BIRA With Optimal Repair Rate Using Fault-Free Memory Region for Area Reduction

As manufacturing process technology scales down, memory capacity and density continue to grow. As the number of memory cells per area increases, so does the possibility of having defects in the memory cells. Therefore, built-in redundancy analysis (BIRA) is widely used for memory test and repair. BIRA repairs faulty cells by replacing them with healthy spare cells. To perform BIRA, the faulty memory cell information is required and is collected via a memory test. By using the faulty cell information, BIRA can analyze and perform the repair by assigning spares to defective cells. However, it requires extra hardware to store the faulty cell information. Most conventional BIRA approaches utilize content-addressable memories (CAMs) to store faulty cell information. Owing to the CAMs’ fast access speed, the repair time can be reduced. However, CAMs are the critical source of an area overhead for BIRA, and a CAM could be shared across multiple memory banks. The shared structure limits simultaneous repair operations for the banks, and when the CAM is faulty, the conventional CAM-based BIRA cannot be performed. This paper proposes a new BIRA method, which does not require extra memory and instead utilizes a fault-free memory region to store the faulty cell information. Because extra memory is not required in the proposed BIRA method, the area overhead is decreased. The proposed hierarchical repair algorithm thus helps to reduce the analysis time, while achieving optimal normalized repair rate.

Hardware-Efficient Built-In Redundancy Analysis for Memory With Various Spares

Memory capacity continues to increase, and many semiconductor manufacturing companies are trying to stack memory dice for larger memory capacities. Therefore, built-in redundancy analysis (BIRA) is of utmost importance because the probability of fault occurrence increases with a larger memory capacity. A traditional spare structure that consists of simple rows and columns is somewhat inadequate for multiple memory blocks BIRA because the hardware overhead and spare allocation efficiency are degraded. The proposed BIRA uses various types of spares and can achieve a higher yield than a simple row and column spare structure. Herein, we propose a BIRA that can achieve an optimal repair rate using various spare types. The proposed analyzer can exhaustively search not only row and column spare types but also global and local spare types. In addition, this paper proposes a fault-storing content-addressable memory (CAM) structure. The proposed CAM is small and collects faults efficiently. The experimental results show a high repair rate with a small hardware overhead and a short analysis time.

A Survey of Repair Analysis Algorithms for Memories

Current rapid advancements in deep submicron technologies have enabled the implementation of very large memory devices and embedded memories. However, the memory growth increases the number of defects, reducing the yield and reliability of such devices. Faulty cells are commonly repaired by using redundant cells, which are embedded in memory arrays by adding spare rows and columns. The repair process requires an efficient redundancy analysis (RA) algorithm. Spare architectures for the repair of faulty memory include one-dimensional (1D) spare architectures, two-dimensional (2D) spare architectures, and configurable spare architectures. Of these types, 2D spare architectures, which prepare extra rows and columns for repair, are popular because of their better repairing efficiency than 1D spare architectures and easier implementation than configurable spare architectures. However, because the complexity of the RA is NP-complete, the RA algorithm should consider various factors in order to determine a repair solution. The performance depends on three factors: analysis time, repair rate, and area overhead. In this article, we survey RA algorithms for memory devices as well as built-in repair algorithms for improving these performance factors. Built-in redundancy analysis techniques for emergent three-dimensional integrated circuits are also discussed. Based on this analysis, we then discuss future research challenges for faulty-memory repair studies.

A 3 Dimensional Built-In Self-Repair Scheme for Yield Improvement of 3 Dimensional Memories

A 3-dimensional Built-In Self-Repair (3D BISR) scheme is proposed for 3-dimensional (3D) memories. The proposed 3D BISR scheme consists of two phases: a parallel test-repair phase, and a serial test-repair phase. After all memory dice are simultaneously tested, only the faulty memory dice are serially tested and repaired using one Built-In Redundancy Analysis (BIRA) module. Thus, it is a faster test-repair with low area overhead. The proposed BIRA algorithm with a post-share redundancy scheme performs exhaustive searches for all combinations of spare rows and columns. Experimental results show that the proposed 3D BISR is up to two times faster than the 3D serial test-serial repair BISR when seven 2048 $\times$ 2048 bit memory dice are stacked. The proposed 3D BISR requires 44.55% of the area in comparison to a 3D parallel test-parallel repair BISR for four stacked memory dice (one 128 K RAM, two 256 K RAMs, and 512 K RAM). The yield of 3D memories is the highest due to the exhaustive search BIRA algorithm with the post-share redundancy scheme as shown in various experimental results.

Optimized Built-In Self-Repair for Multiple Memories

A new built-in self-repair (BISR) scheme is proposed for multiple embedded memories to find optimum point of the performance of BISR for multiple embedded memories. All memories are concurrently tested by the small dedicated built-in self-test to figure out the faulty memories, the number of faults, and irreparability. After all memories are tested, only faulty memories are serially tested and repaired by the shared built-in redundancy analysis according to the sizes of memories in descending order. Thus, the fast test and repair are performed with low area overhead. To accomplish an optimal repair rate and a fast analysis speed, an exhaustive search for all combinations of spare rows and columns is proposed based on the optimized fault collection. Experimental results show that the proposed BISR has the optimal repair rate because of the exhaustive search. The performance of the proposed BISR is located in the optimum point between the test and repair time, and the area overhead. For example, the proposed BISR requires 49.6% of the area and 1.3 times of the test and repair time in comparison with parallel BISR scheme for four memories (one 128 K, two 256 K, and one 512 K memories). Furthermore, the more there are memories, the more superior performance in terms of the test and repair time, and the area overhead is shown.

A BIRA for Memories With an Optimal Repair Rate Using Spare Memories for Area Reduction

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.

패턴 테스트 가능한 NAND-형 플래시 메모리 내장 자체 테스트

메모리반도체산업이 성장함에 따라 수요와 공급이 큰 폭으로 증가하고 있다. 그 중 플래시 메모리가 스마트폰, 테블릿PC, SoC(System on Chip)산업에 많이 사용되고 있다. 플래시 메모리는 NOR-형 플래시 메모리와 NAND-형 플래시 메모리로 나뉜다. NOR-형 플래시 메모리는 BIST(Built-In Self Test), BISR(Built-In Self Repair), BIRA(Built-In Redundancy Analysis)등 많은 연구가 진행되었지만 NAND-형 플래시 메모리 BIST는 연구가 진행되지 않았다. 현재 NAND-형 플래시 메모리 패턴테스트는 고가의 외부 테스트 장비를 사용하여 테스트를 수행하고 있다. NAND-형 플래시 메모리에서는 블록단위로 소거, 페이지 단위로 읽기, 쓰기 동작이 가능하기 때문에 자체 내장 테스트가 존재하지 않고 외부장비에 의존하고 있다. 고가의 외부패턴 테스트 장비에 의존해서 테스트를 수행하던 NAND-형 플래시 메모리를 외부 패턴 테스트 장비 없이 패턴 테스트를 수행할 수 있도록 두 가지의 유한 상태 머신 기반 구조를 갖고 있는 BIST를 제안한다.

Built In Self Repair Architecture for SRAM Using Redundancy Logic

With scaling of device dimensions in nanometer technologies, a large number of cells in memory array are expected to be faulty due to process parameter variations. Hence this paper analyzes SRAM (Static Random Access Memory) cell failure, by mapping them to fault models and proposes a new BISR (Built In Self Repair) architecture for SRAM, suitable for high performance application. Proposed BISR design is composed of a BIST (Built In Self Test) module and BIRA (Built In Redundancy Analysis) module. March tests are used in BIST to test memory chips. Faulty addresses are stored in the redundancy logic. The BIRA module executes an efficient redundancy analysis algorithm proposed for SRAM based on spare rows. The process of detecting the faulty line and redirecting to the next available word line are done during the reset period. Hence the power consumption and the area penalty are kept minimum. The BISR concept is described in RTL code. The redundancy and BIST logic is fully synthesizable and can be reused.

High-efficiency BIRA for embedded memories with a high repair rate and low area overhead

High-efficiency built-in redundancy analysis (BIRA) is presented. The proposed BIRA uses three techniques to achieve a high repair rate using spare mapping registers with adjustable fault tags to reduce area overhead. Simulation results show that the proposed BIRA is a reasonable solution for embedded memories.

비트맵 메모리 공유를 통해 면적을 크게 줄인 효율적인 수리 방법

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.

Cost-Efficient Built-In Redundancy Analysis With Optimal Repair Rate for RAMs

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.

A Low-Cost Built-In Redundancy-Analysis Scheme for Word-Oriented RAMs With 2-D Redundancy

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.