Using embedded objects for yield monitoring

Bazan, G.; Gravel, F.; Huisman, L.M.; Pardee, A.; Pastel, Leah; Rowe, K.

doi:10.1109/asmc.2004.1309549

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…With each failing scan chain containing several hundreds to thousands of latches, identifying systematics using scan chain diagnostics on all the chips failing chain tests is very time-consuming. Instance Analysis [1,4] was used to identify scan chain systematics and drive root-cause analysis. Based on product test scan failure data, pass fail rates of each scan chain are calculated, normalized and subsequently analyzed for a number of wafers collectively to identify systematic issues.…”

Section: A Breakthrough-1: Chain Instance Analysismentioning

confidence: 99%

Successful yield ramp using product test, scan and memory diagnosis

Muthumalai

Iverson

Sinnott

et al. 2014

25th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2014)

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Fast yield ramp is very important for a foundry during advanced process development and volume production. Logic and SRAM circuits are the major design blocks in advanced Integrated Circuits.Identification of systematic failures in logic and SRAM blocks using product test and diagnostics is critical to increasing the speed of yield learning in sub-40nm ICs. This paper highlights the importance of using logic and SRAM diagnosis for product yield ramp using three yield breakthroughs.

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Section: A Breakthrough-1: Chain Instance Analysismentioning

confidence: 99%

Successful yield ramp using product test, scan and memory diagnosis

Muthumalai

Iverson

Sinnott

et al. 2014

25th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2014)

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“…One of the most pervasively used and least expensive diagnos tic technique to identify systematics at IBM is Instance Anal ysis (IA) [8]. IA is a test data analysis technique, wherein, pass/fail rates of instances of embedded obj ects are calculated and subsequently analyzed for a number of wafers collectively to identify systematics.…”

Section: Subdie Instance Analysismentioning

confidence: 99%

Hard to find, easy to find systematics; just find them

Desineni

Pastel

Kassab

et al. 2010

2010 IEEE International Test Conference

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In a manufacturing organization, every morning starts with the question: what is the yield today? The cost of wafer manufacturing being fairly constant, product yield is one of the most significant variables for profitability. With the yield paretos increasingly dominated by systematic defects, yield learning based on product test is fast becoming a fundamen tal requirement. For an integrated device manufacturer like IBM, product-based yield learning is even more critical as this drives technology learning as well. In this paper, we will present some of IBM's yield learning techniques and several case studies from high-volume manufacturing. These tech niques extend from test data analysis, to analysis of scan based product diagnosis results, to detailed layout analysis in conjunction with test, diagnosis and inline defect inspection data. We will discuss the increasing levels of complexity asso ciated with the various techniques and argue that an effective yield learning strategy must comprise all of the above.

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