Handling Discontinuous Effects in Modeling Spatial Correlation of Wafer-level Analog/RF Tests

Huang, Ke; Kupp, Nathan; Carulli, John M.; Makris, Yiorgos

doi:10.7873/date.2013.123

Cited by 20 publications

(35 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example, we can refer a recent paper that shows that the wafer fabrication process using a stepper induces patterned variations across it. The authors define similarity clusters (also by machine-learning) and train specific models for each cluster [2]. This implicitly means that the localization of the circuit on the wafer matters in the Alternate Test approach, and should thus be taken as an input.…”

Section: Introductionmentioning

confidence: 99%

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Léger

Barragán

2016

Integration

View full text Add to dashboard Cite

International audienceMachine-learning indirect test relies on powerful statistical algorithms to build prediction models that relate cheap measurements to costly performance metrics. Though many works in the past have been focused on proposing different models or on ways to improve the reliability of the results, it appears that the main bottleneck of the approach is the definition of an information-rich input space. Finding the appropriate measurements that are both cheap and meaningful is a task that has not yet been automated. In this framework, feature selection is a necessary tool to explore possible candidates. In this paper a hybrid method is proposed that lay between filtering and wrapper-based methods, trying to strike the right balance between accuracy and speed for the particular case of Alternate Test

show abstract

Section: Introductionmentioning

confidence: 99%

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Léger

Barragán

2016

Integration

View full text Add to dashboard Cite

show abstract

“…Various test cost reduction methods have been proposed that apply data analytics, machine learning algorithms, and statistical methods [1]- [3]. In particular, the wafer-level characteristic modeling method based on a statistical algorithm is the most promising candidate that reduces the test cost, that is, measurement cost, without impairing the test quality [4]- [11]. In these studies, a statistical modeling technique was used to predict the entire measurement on a wafer from a small number of sample measurements.…”

Section: Introductionmentioning

confidence: 99%

“…In [5]- [8], a statistical prediction method, called a virtual prove, based on compressed sensing [17], [18] was proposed. The Gaussian process (GP)-based method [19] provides more accurate prediction results [9]- [11]. The use of GP modeling has another side benefit.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wafer-level Variation Modeling for Multi-site RF IC Testing via Hierarchical Gaussian Process

Shintani¹,

Mian²,

Nakamura³

et al. 2021

Preprint

View full text Add to dashboard Cite

Wafer-level performance prediction has been attracting attention to reduce measurement costs without compromising test quality in production tests. Although several efficient methods have been proposed, the site-to-site variation, which is often observed in multi-site testing for radio frequency circuits, has not yet been sufficiently addressed. In this paper, we propose a wafer-level performance prediction method for multi-site testing that can consider the site-to-site variation. The proposed method is based on the Gaussian process, which is widely used for waferlevel spatial correlation modeling, improving the prediction accuracy by extending hierarchical modeling to exploit the test site information provided by test engineers. In addition, we propose an active test-site sampling method to maximize measurement cost reduction. Through experiments using industrial production test data, we demonstrate that the proposed method can reduce the estimation error to 1/19 of that obtained using a conventional method. Moreover, we demonstrate that the proposed sampling method can reduce the number of the measurements by 97% while achieving sufficient estimation accuracy.

show abstract

“…Kupp et al [7] extended their work by introducing radial variation modeling; the method in [7] fits a Gaussian process model using observed data on a wafer and predicts unobserved data on the wafer using the model. This paper is also extended to handle discontinuity effects [6] and to combine with alternate tests [5]. Virtual probe (VP) [8] is a recent technique based on compressed sensing (CS), a recent breakthrough in signal processing and information theory.…”

Section: Introductionmentioning

confidence: 99%

3-D Probe: Low-Cost Variation Modeling Using Intertest-Item Correlations

Chung

Kim

Yang

2014

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

View full text Add to dashboard Cite

Process variation models for variation tolerant designs are developed through expensive silicon characterization. This paper presents a low-cost variation characterization method that takes advantage of correlations between test items. The proposed method is based on compressed sensing (CS), a new innovative theory in signal processing and information theory, and we formulate the problem of accounting for the correlations in the form of standard CS problems, allowing us to leverage advances in CS theory. We consider wafer-level measurement results for multiple test items a 3-D signal and propose the sparsifying transform that combines the 2-D discrete cosine transform and the Karhunen-Loéve transform. Our experimental results show that the proposed method reduces the number of samples required for the same accuracy up to 2X compared to virtual probe when two test items are used. IndexTerms-Approximate computing, compressed sensing (CS), intertest-item correlations, VLSI design.

show abstract

Handling Discontinuous Effects in Modeling Spatial Correlation of Wafer-level Analog/RF Tests

Cited by 20 publications

References 0 publications

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Wafer-level Variation Modeling for Multi-site RF IC Testing via Hierarchical Gaussian Process

3-D Probe: Low-Cost Variation Modeling Using Intertest-Item Correlations

Contact Info

Product

Resources

About