Yield forecasting in fab-to-fab production migration based on Bayesian Model Fusion

Ahmadi, Ali; Stratigopoulos, Haralampos-G.; Nahar, Amit; Orr, Bob; Pas, M. F.; Makris, Yiorgos

doi:10.1109/iccad.2015.7372543

Cited by 4 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Fab-to-Fab Production Migration: Demand fluctuations and other financial, geographical or political reasons often cause a production to be migrated from one fabrication plant to another, wherein a device may have never been fabricated before [3], [4]. Forecasting how well a device will yield in the target plant is extremely valuable for production planning and yield ramp-up purposes.…”

Section: Introductionmentioning

confidence: 99%

Yield Forecasting Across Semiconductor Fabrication Plants and Design Generations

Ahmadi

Stratigopoulos

Huang

et al. 2017

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Self Cite

View full text Add to dashboard Cite

Yield estimation is an indispensable piece of information at the onset of high-volume production of a device, as it can inform timely process and design refinements in order to achieve high yield, rapid ramp-up and fast time-to-market. To date, yield estimation is generally performed through simulationbased methods. However, such methods are not only very timeconsuming for certain circuit classes, but also limited by the accuracy of the statistical models provided in the process design kits. In contrast, herein we introduce yield estimation solutions which rely exclusively on silicon measurements and we apply them towards predicting yield during (i) production migration from one fabrication facility to another, and (ii) transition from one design generation to the next. These solutions are applicable to any circuit, regardless of process design kit accuracy and transistor-level simulation complexity, and range from rather straightforward to more sophisticated ones, capable of leveraging additional sources of silicon data. Effectiveness of the proposed yield forecasting methods is evaluated using actual high-volume production data from two 65nm RF transceiver devices.

show abstract

Section: Introductionmentioning

confidence: 99%

Yield Forecasting Across Semiconductor Fabrication Plants and Design Generations

Ahmadi

Stratigopoulos

Huang

et al. 2017

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…1. Estimating the yield of a product of which the fabrication is to be delivered to another factory (Ahmadi et al 2015). 2.…”

Section: Introductionmentioning

confidence: 99%

“…These estimates are aggregated using fuzzy intersection (FI). The aggregation result is then defuzzified using another ANN (Ahmadi et al 2015). The procedure for the proposed FCI approach is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example

Chen

Wang

2017

Oper Res Int J

View full text Add to dashboard Cite

To increase the ecological sustainability of manufacturing, enhancing the yield of each product is a critical task that eliminates waste and increases profitability. An equally crucial task is to estimate the future yield of each product so that the majority of factory capacity can be allocated to products that are expected to have higher yields. To this end, a fuzzy collaborative intelligence (FCI) approach is proposed in this study. In this FCI approach, a group of domain experts is formed. Each expert constructs an artificial neural work (ANN) to fit an uncertain yield learning process for estimating the future yield with a fuzzy value; in past studies, however, uncertain yield learning processes were modeled only by solving mathematical programming problems. In this research, fuzzy yield estimates from different experts were aggregated using fuzzy intersection. Then, the aggregated result was defuzzified with another ANN. A real dynamic random access memory case was utilized to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology outperformed five existing methods in improving the estimation accuracy, which was measured in terms of the mean absolute error and the mean absolute percentage error.

show abstract

Variation and failure characterization through pattern classification of test data from multiple test stages

Hsu

Sarson

Schatzberger

et al. 2016

2016 IEEE International Test Conference (ITC)

View full text Add to dashboard Cite

Yield forecasting in fab-to-fab production migration based on Bayesian Model Fusion

Cited by 4 publications

References 9 publications

Yield Forecasting Across Semiconductor Fabrication Plants and Design Generations

Yield Forecasting Across Semiconductor Fabrication Plants and Design Generations

A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example

Variation and failure characterization through pattern classification of test data from multiple test stages

Contact Info

Product

Resources

About