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Cell-aware diagnosis results on a partial cell layout (left) and PFA results (right). The open suspect S1.6 highlighted in red corresponds to the open contact in the PFA image.


before these are merged in a way that identifies the best matching symptoms and suspects.


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Cell-Aware Diagnosis Cell-aware diagnosis can pin-


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point the potential defect location inside the suspect cell, such as an open defect at a specific transistor, shorts, etc. Beyond the typical diag- nosis report, which lists all callouts for both interconnect and cell-inter- nal defects, a chip-level layout mark- er file can be generated to highlight all potential defect locations for guiding the PFA process. The cell- aware diagnosis reports can be used for volume yield analysis similar to how traditional reports are used. Several cell-aware diagnosis


results have been published, based on technologies from 160 nm down to 10 nm FinFET. In all of the 17 cases examined in this experiment, the actual defect was included in the diagnosis results. On average, the diagnosis resolution for cell internal defects was improved by 11.3X. In one published case study, (H.


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 


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Tang, et.al., “Diagnosing Cell Inter - nal Defects Using Analog Simulation- based Fault Models,” Asian Test Symposium 2014) several diagnosis results from an AMD APU manufac- tured in a 28 nm process were com- pared with PFA results to compare diagnosis accuracy and resolution. One of these is a defect within a


scan flip-flop. This device passed chain tests but failed scan tests. Regular layout-aware diagnosis called out a scan flip-flop cell and its SEN input pin. This scan flip-flop cell has 38 transistors. Cell-aware diagnosis called out


13 cell-internal suspects out of a total of 350 defined fault locations inside the cell. The 13 suspects included one transistor stuck on, one transistor stuck off, five bridge, and six open faults. That corresponds to a resolution improvement of 350/13 = 27X. A close look at these suspects reveals that the majority (10 out of


when a defect is known to be within a cell, locating a defect in such a complex cell is daunting. Deter - mining whether the defect is system- atic or yield-limiting typically re - quires the examination of a large number of failing dice, which can take several months. Cell-aware diagnosis software


dramatically improves the ability to locate specific failing transistors, a level of precision that enables auto- motive IC makers to comply with the strict quality, reliability and safety standards set by OEMs and the ISO 26262 standard. Cell-aware diagnosis has been


validated successfully on several real silicon failures with known PFA results. The diagnosis resolution for cell internal defects can be improved by up to 70X on a complex full adder cell, and by over 10X on average, with a small time overhead.


Cell-aware diagnosis software dramatically improves the ability to


locate specific failing tran- sistors, a level of precision that enables automotive IC makers to comply with the strict quality, reliability


and safety standards set by OEMs and the ISO 26262 standard.


Cell-aware diagnosis is enabled


by a layout-justified, cell-aware fault model, which is generated once per library. Therefore, there is no impact on the data collection and diagnosis flow. Cell-aware diagnosis improves results for single-part diagnosis sce- narios, such as customer return analysis, as well as for yield analysis. Contact: Mentor, a Siemens


Business, 8005 SW Boeckman Road, Wilsonville, OR 97070 % 503-685- 7000 E-mail: tessent@mentor.com Web: www.mentor.com r


www.us-tech.com


Using Cell-Aware Scan Diagnosis...


Continued from page 58


of suspects along with layout infor- mation.


The only unique step to enable


cell-aware diagnosis is to also read in the cell-aware fault models. For a given failure file, the cell-aware diagnosis algorithm first tries to find initial candidates by path tracing every failing pattern, and creates suspects by simulating failing pat- terns to find failing pattern matches. Cell-aware diagnosis creates


another set of suspect cell instances based on the cell-aware fault models and simulates the failing patterns for them. Then, passing patterns are simulated on both suspect lists


13) are connected to one cell- inter- nal net, and the other three suspects are connected to a second net — both connected to the same transistor.


ISO 26262 Application ISO 26262 is a standard for


quality and reliability in automotive electronics. Key requirements in clude test quality, in-field self test and field return (RMA) analysis. Automotive ICs therefore use testability solutions that are otherwise reserved for more complex designs and leading-edge manufacturing nodes. Consider large, complex cells


such as adders, multipliers and multi-bit sequential elements. Even


July, 2017


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