In this paper, we propose a unified approach to partitioning, floorplanning, and retiming for effective and efficient performance optimization. The integration enables the partitioner to exploit more realistic geometric delay model provided by the underlying floorplan. Simultaneous consideration of partitioning and retiming under the geometric delay model enables us to hide global interconnect latency effectively by repositioning FF along long wires. Under the proposed geometric embedding based performance driven partitioning problem, our GEO algorithm performs multi-level top-down partitioning while determining the location of the partitions. We adopt the concept of sequential arrival time [14] and develop sequential required time in our retiming based timing analysis engine. GEO performs cluster-move based iterative improvement on top of multi-level cluster hierarchy [4], where the gain function obtained from the timing analysis is based on the minimization of cutsize, wirelength, and sequential slack. In our comparison to (i) state-of-the-art partitioner hMetis [9] followed by retiming [11] and simulated annealing based slicing floorplanning [15], and (ii) state-of-the-art simultaneous partitioning with retiming HPM [7] followed by floorplanning [15], GEO obtains 35% and 23% better delay results while maintaining comparable cutsize, wirelength, and runtime results.

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Physical planning with retiming

Diagnosis technique plays a key role during the rapid development of the semiconductor memories, for catching the design and manufacturing failures and improving the overall yield and quality. Investigation on efficient diagnosis algorithms is very important due to the expensive and complex fault/failure analysis process. We propose March-based RAM diagnosis algorithms which not only locate faulty cells but also identify their types. The diagnosis complexity is O(17N) and O((17+10B)N) for bit-oriented and word-oriented diagnosis algorithms, respectively, where N represents the address number and B is the data width. Using the proposed algorithms, stuck at faults, state coupling faults, idempotent coupling faults and inversion coupling faults can be distinguished. Furthermore, the coupled and coupling cells can be located in the memory array. Our word-oriented diagnosis algorithm can distinguish all of the inter-word and intra-word coupling faults, and locate the coupling cells of the intra-word inversion and idempotent coupling faults. With additional 2B-1 operations, the algorithm can further locate the intra-word state coupling faults. With improved diagnostic resolution and test time, the proposed algorithms facilitate the development and manufacturing of semiconductor memories.

March-based RAM diagnosis algorithms for stuck-at and coupling faults

Embedded memory test and diagnosis is becoming an important issue in system-on-chip (SOC) development. Direct access of the memory cores from the limited number of I/O pins is usually not feasible. Built-in self-diagnosis (BISD), which include built-in self-test (BIST), is rapidly becoming the most acceptable solution. We propose a BISD design and a fault diagnosis system for embedded SRAM. It supports manufacturing test as well as diagnosis for design verification and yield improvement. The proposed BISD circuit is on-line programmable for its March test algorithms. Test chips have been designed and implemented. Our experimental results show that the BISD hardware overhead is about 2.4% for a typical 128 Kb SRAM and only 0.65% for a 2 Mb SRAM.

A built-in self-test and self-diagnosis scheme for embedded SRAM

Built-in self-test and self-diagnosis scheme for embedded SRAM

We present an error catch and analysis (ECA) system for semiconductor memories. The system consists of a test algorithm generator called TAGS, a fault simulator called RAM-SES, and an error analyzer (ERA). We use TAGS to generate a set of test algorithms of different lengths and diagnostic resolutions for the memory under test, and use RAMSES to generate the March dictionary for each test algorithm. With the March dictionaries, ERA is able to support March algorithms for easy diagnosis of faulty RAMs. Legacy test algorithms also can be reused. When integrated with a RAM tester, our ECA system can generate RAM bitmaps that are similar to the RAM layout. The bitmaps provide detail information about the error locations and faults causing the errors. Based on the information, diagnosis of the RAM chips for yield and reliability improvement can be done more easily.

Error catch and analysis for semiconductor memories using march tests

The increasing use of large embedded memories in systems-on-chips requires automatic memory reconfiguration to avoid the need for external accessibility. In this work, effective diagnostic memory tests of linear order O(N) are proposed that enable memory reconfiguration, and their diagnostic capabilities are analyzed. In particular, these tests allow single-cell faults to be distinguished from multiple-cell faults, such as coupling faults. In contrast to conventional O(N) tests, all cells involved in a fault are detected and localized, which allows complete reconfiguration using minimal-area BIST hardware that compares favorably with other BIST designs.

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Diagnostic testing of embedded memories using BIST

Conventionally, Automatic Test Equipment (ATE) has been used for parametric tests of VLSI systems to determine the influence of clock speed, supply voltage, and temperature on the specified functionality of the circuit under test. This method is likely to become infeasible in the near future due to an aggressive drive to increased Overall Timing Accuracy (OTA), as predicted in the SIA Roadmap. In this paper, a method for Parametric Built-in Self-Test using on-chip Phase-Locked Loops (PLLs) is presented which is capable of overcoming the timing accuracy problem. A PLL-based test circuitry to determine the maximum frequency is described. Design constraints of the PLL control system, such as stability and resolution, are discussed for a specific design using 0.35 /spl mu/m CMOS technology. The functionality of the self-test circuitry is demonstrated to be competitive with parametric ATE tests such as Global Search Track without the need for expensive test equipment.

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Parametric built-in self-test of VLSI systems

Systems-on-Chips often contain a large amount of embedded memory. In order to obtain sufficiently high yield, efficient diagnosis and repair facilities are needed for the memories. A novel and efficient approach for collecting complete failure data during on-chip memory testing is proposed that can be combined with a row/column reconfiguration algorithm for complete on-chip memory repair. A sequence of diagnostic tests of linear order is utilized that detects and localizes all cells involved in single-cell faults and two-cell coupling faults, such as idempotent coupling faults, and provides this information to on-chip circuitry for memory repair. Failure data are collected at the operating speed of the memory-under-test so that tests can be applied at speed. The data acquisition circuitry evaluates the test results and classifies faults as column failures, coupling faults, or single-cell faults for near-optimal allocation of spare resources. The proposed test and data acquisition algorithm can be realized as compact Built-In Self-Test (BIST) circuitry using standard design libraries.

A data acquisition methodology for on-chip repair of embedded memories

A circuit system and a method of analyzing audio or video input data that is capable of detecting, classifying, and post-processing patterns in an input data stream. The circuit system may consist of one or more digital processors, one or more configurable spiking neural network circuits, and digital logic for the selection of two-dimensional input data. The system may use the neural network circuits for detecting and classifying patterns and one or more the digital processors to perform further detailed analyses on the input data and for signaling the result of an analysis to outputs of the system.

Dirk Niggemeyer

Papers

Diagnostic testing of embedded memories using BIST

Parametric built-in self-test of VLSI systems

A data acquisition methodology for on-chip repair of embedded memories

System-on-a-chip incorporating artificial neural network and general-purpose processor circuitry