Chin-Lung Su

A processor-based built-in self-repair design for embedded memories

We propose an embedded processor-based built-in self-repair (BISR) design for embedded memories. In the proposed design we reuse the embedded processor that can be found on almost every system-on-chip (SOC) product, in addition to many distinct features. By reusing the embedded processor, the controller and redundancy analysis circuit of a typical BISR design can be removed. Also, the test algorithm and redundancy analysis/allocation algorithm are easily programmable, greatly increasing the design flexibility. We also have developed a memory wrapper that allows at-speed testing of the memory cores. The area overhead of the proposed BISR scheme is low, since only the memory wrapper needs to be realized explicitly. Our experiments show that the BISR area overhead for a typical 8 K/spl times/32 SRAM is lower than 1%.

Testing MRAM for Write Disturbance Fault

The magnetic random access memory (MRAM) is considered one of the potential candidates that will replace current on-chip memories (RAM, EEPROM, and flash memory) in the future. The MRAM is fast and does not need a high supply voltage for read/write operations. It can also endure almost unlimited read/write cycles. These combined advantages of RAM and flash memory make it a potential choice for SOC. In this paper, we present the write disturbance fault (WDF) model for MRAM, i.e., a fault that affects the data stored in the MRAM cells due to excessive magnetic field during the write operation. The proposed WDF model is justified by chip measurement results. We also construct the SPICE macro model for the magnetic tunneling junction (MTJ) device of the toggle MRAM to obtain circuit simulation results. An MRAM chip has been designed and fabricated using a CMOS-based 0.18mum technology. We also present an MRAM fault simulator called RAMSES-M, based on which we derive the shortest test for the proposed WDF model. The test is shown to be better and more robust as compared with March C. Finally, we present a March 17N diagnosis algorithm for identifying the WDF

MRAM defect analysis and fault modeling

With the advent of system-on-chip (SOC), the demand for embedded memory cores increases rapidly. The magnetic random access memory (MRAM) is considered one of the potential candidates that replace current on-chip memories (RAM, EEPROM, and flash memory) in the future. The MRAM has a high speed and does not need high supply voltage for read/write operations, so it has the advantages of RAM and flash memory, making it a potentially good choice for SOC. The testing of MRAM, however, has not been fully investigated. In this work we classify and analyze the MRAM defects and their behavior, and propose its fault models. We have built a SPICE model of MRAM cell and performed defect injection and simulation of a real MRAM circuit. The circuit has been implemented and fabricated with a novel 0.18 m technology. The simulation results regarding the correlation between the defects and conventional fault models show that most of the defects can be covered by the stuck-at fault model. The test data based on the fabricated chips show that the stuck-at faults do cover most of the defects on the chips. However, from the experiment we also have identified two new faults, i.e., the Multi-Victims fault and Kink fault.

Write Disturbance Modeling and Testing for MRAM

The magnetic random access memory (MRAM) is considered one of the potential candidates that will replace current on-chip memories (RAM, EEPROM, and flash memory) in the future. The MRAM is fast and does not need a high supply voltage for read/write operations, and is compatible with the CMOS technology. It can also endure almost unlimited read/write cycles. These combined advantages of RAM and flash memory make it a potential choice for SOC. In this paper, we present the write disturbance fault (WDF) model for MRAM, i.e., a fault that affects the data stored in the MRAM cells due to excessive magnetic field during the write operation. We also construct the SPICE macro model for the magnetic tunneling junction (MTJ) device of the toggle MRAM to obtain circuit simulation results. We then present an MRAM fault simulator called RAMSES-M, based on which we derive the shortest test for the proposed WDF model. The test is shown to be better and more robust as compared with the conventional March C-test algorithm. We also present a March 17 N diagnosis algorithm for identifying WDF. A 1 Mb MRAM chip has been designed and fabricated using a CMOS-based 0.18-mum technology. The proposed WDF model is justified by chip measurement results, with the march test results reported. Finally, specific MRAM fault behavior and test issues are discussed.

A Built-in Self-Diagnosis and Repair Design With Fail Pattern Identification for Memories

With the advent of deep-submicrometer VLSI technology, the capacity and performance of semiconductor memory chips is increasing drastically. This advantage also makes it harder to maintain good yield. Diagnostics and redundancy repair methodologies thus are getting more and more important for memories, including embedded ones that are popular in system chips. In this paper, we propose an efficient memory diagnosis and repair scheme based on fail-pattern identification. The proposed diagnosis scheme can distinguish among row, column, and word faults, and subsequently apply the Huffman compression method for fault syndrome compression. This approach reduces the amount of data that need to be transmitted from the chip under test to the automatic test equipment (ATE) without losing fault information. It also simplifies the analysis that has to be performed on the ATE. The proposed redundancy repair scheme is assisted by fail-pattern identification approach and a flexible redundancy structure. The area overhead for our built-in self-repair (BISR) design is reasonable. Our repair scheme uses less redundancy than other redundancy schemes under the same repair rate requirement. Experimental results show that the area overhead of the BISR design is only 4.1% for an 8 K × 64 memory and is in inverse proportion to the memory size.

A memory built-in self-diagnosis design with syndrome compression

We present a memory built-in self-diagnosis (BISD) design that incorporates a fault syndrome compression scheme. We also have developed efficient faulty-word, faulty-row, and faulty-column identification methods, which have been incorporated in our new BISD design. Our approach reduces the amount of data that need to be transmitted from the chip under test to the automatic test equipment (ATE). It therefore reduces the ATE occupation time and the required ATE capture memory space. It also simplifies the analysis that has to be performed on the ATE. Simulation results for memories under various fault pattern distributions show that in most cases the data can be compressed to less than 6% of its original size.

Fail pattern identification for memory built-in self-repair

With the advent of deep submicron technology and system-on-chip (SOC) design methodology, we are seeing on-chip memory cores to represent a growing percentage of the chip area. The yield of an SOC is usually dominated by the memory yield, so the improvement of memory yield is crucial in SOC development. In this work, we propose a built-in self-repair (BISR) scheme for memory yield improving. The novelty of our approach is that we can identify the fail patterns so that appropriate spare elements (e.g., spare rows, columns, words, or blocks) can be allocated to repair the defective memory. Some BISR methods are discussed and compared. We select the scheme that uses fewer spare elements than others given the same repair rate. The area overhead of the BISR scheme is only 2.2% for an 8K/spl times/64 memory.

Diagnosis of MRAM Write Disturbance Fault

To help improve quality and yield of magnetic random access memory (MRAM), we propose an adaptive diagnosis algorithm (ADA) that can efficiently identify the write disturbance fault (WDF) for MRAM. The proposed test algorithm is a March-based one, i.e., it has linear time complexity and can easily be implemented with built-in self-test (BIST). However, the proposed test method can evaluate the process stability and uniformity using logical test method. We also develop a BIST circuit that supports the proposed WDF diagnosis test method. We propose the BIST scheme based on the decision write mechanism of the toggle MRAM to reduce total test time. A 1 Mb toggle MRAM prototype chip with the proposed BIST circuit has been designed and fabricated using a special 0.15 mum CMOS technology. The BIST circuit overhead is only about 0.04% with respect to the 1 Mb MRAM. The test time is reduced by about 30% as compared with the test method without using the decision write mechanism.

Test and Diagnosis Algorithm Generation and Evaluation for MRAM Write Disturbance Fault

We proposed the systematic tools, RAMSES-M and TAGS-M, for test and diagnosis algorithms evaluation and development, respectively. In addition to traditional memory fault models, the tools support the MRAM specific fault model, Write Disturbance Fault (WDF) and its specific test operation, Read-previous, which is proposed in this paper, too. The concept of Weighted Fault Coverage (WFC) is introduced and adopted by RAMSES-M. Several test and diagnosis algorithms generated by the proposed tool are compared with other conventional March algorithms. The results show that the proposed algorithms have better performance for testing and diagnosis.

Embedded memory diagnostic data compression using differential address

Embedded memory diagnostics is normally done by the built-in self-diagnosis (BISD) hardware, which collects and sends the diagnostic data to the external tester. The cost of the diagnosis process highly depends on the data volume sent between the chip under test and the tester, since the transmission time and the tester capture memory are major cost factors. We propose a memory BISD design using differential addressing, as well as a method for evaluating and choosing a proper differential address level. Based on our previous work on pattern identification BISD and syndrome compression design, the proposed differential address compression scheme further reduces the diagnostic data volume. Experimental results show that the BISD design is cost-effective.