Scholars@Duke publication: Efficient Process Variation Characterization by Virtual Probe

Machine Learning in VLSI Computer-Aided Design

Efficient Process Variation Characterization by Virtual Probe

Publication , Chapter

Tao, J; Zhang, W; Li, X; Liu, F; Acar, E; Rutenbar, RA; Blanton, RD; Zeng, X

January 1, 2019

In this chapter, we propose a new technique, referred to as virtual probe (VP), to efficiently measure, characterize, and monitor spatially correlated inter-die and/or intra-die variations in nanoscale manufacturing process. VP exploits recent breakthroughs in compressed sensing to accurately predict spatial variations from an exceptionally small set of measurement data, thereby reducing the cost of silicon characterization. By exploring the underlying sparse pattern in spatial frequency domain, VP achieves substantially lower sampling frequency than the well-known Nyquist rate. In addition, VP is formulated as a linear programming problem and, therefore, can be solved both robustly and efficiently. Our industrial measurement data demonstrate the superior accuracy of VP over several traditional methods including two-dimensional interpolation, Kriging prediction, and k-LSE estimation.

Duke Scholars

Author Xin Li Electrical and Computer Engineering

DOI

10.1007/978-3-030-04666-8_7

ISBN

9783030046651

Publication Date

January 1, 2019

Start / End Page

201 / 231

Citation

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Chicago

ICMJE

MLA

NLM

Tao, J., Zhang, W., Li, X., Liu, F., Acar, E., Rutenbar, R. A., … Zeng, X. (2019). Efficient Process Variation Characterization by Virtual Probe. In Machine Learning in VLSI Computer-Aided Design (pp. 201–231). https://doi.org/10.1007/978-3-030-04666-8_7

Tao, J., W. Zhang, X. Li, F. Liu, E. Acar, R. A. Rutenbar, R. D. Blanton, and X. Zeng. “Efficient Process Variation Characterization by Virtual Probe.” In Machine Learning in VLSI Computer-Aided Design, 201–31, 2019. https://doi.org/10.1007/978-3-030-04666-8_7.

Tao J, Zhang W, Li X, Liu F, Acar E, Rutenbar RA, et al. Efficient Process Variation Characterization by Virtual Probe. In: Machine Learning in VLSI Computer-Aided Design. 2019. p. 201–31.

Tao, J., et al. “Efficient Process Variation Characterization by Virtual Probe.” Machine Learning in VLSI Computer-Aided Design, 2019, pp. 201–31. Scopus, doi:10.1007/978-3-030-04666-8_7.

Tao J, Zhang W, Li X, Liu F, Acar E, Rutenbar RA, Blanton RD, Zeng X. Efficient Process Variation Characterization by Virtual Probe. Machine Learning in VLSI Computer-Aided Design. 2019. p. 201–231.

DOI

10.1007/978-3-030-04666-8_7

ISBN

9783030046651

Publication Date

January 1, 2019

Start / End Page

201 / 231