Statistical Analysis with Missing Data

Statistical Analysis With Missing Data (2nd ed.) (Book)

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

As the electronic component supply chain grows more complex due to globalization, with parts coming from a diverse set of suppliers, counterfeit electronics have become a major challenge that calls for immediate solutions. Currently, there are a few standards and programs available that address the testing for such counterfeit parts. However, not enough research has yet addressed the detection and avoidance of all counterfeit partsVrecycled, remarked, overproduced, cloned, out-of-spec/defective, and forged documentationVcurrently infiltrating the electronic component supply chain. Even if they work initially, all these parts may have reduced lifetime and pose reliability risks. In this tutorial, we will provide a review of some of the existing counterfeit detection and avoidance methods. We will also discuss the challenges ahead for im- plementing these methods, as well as the development of new detection and avoidance mechanisms.

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Counterfeit Integrated Circuits: A Rising Threat in the Global Semiconductor Supply Chain

Statistical side channel fingerprinting is a popular hardware Trojan detection method, wherein a parametric signature of a chip is collected and compared to a trusted region in a multi-dimensional space. This trusted region is statistically established so that, despite the uncertainty incurred by process variations, the fingerprint of Trojan-free chips is expected to fall within this region while the fingerprint of Trojan-infested chips is expected to fall outside. Learning this trusted region, however, assumes availability of a small set of trusted (i.e. "golden") chips. Herein, we rescind this assumption and we demonstrate that an almost equally effective trusted region can be learned through a combination of a trusted simulation model, measurements from process control monitors (PCMs) which are typically present either on die or on wafer kerf, and advanced statistical tail modeling techniques. Effectiveness of this method is evaluated using silicon measurements from two hardware Trojan-infested versions of a wireless cryptographic integrated circuit.

Hardware Trojan Detection through Golden Chip-Free Statistical Side-Channel Fingerprinting

We present a method to detect a common type of counterfeit Integrated Circuits (ICs), namely used ones, from their brand new counterparts using Support Vector Machines (SVMs) In particular, we demonstrate that we can train a one-class SVM classifier using only a distribution of process variation-affected brand new devices, but without prior information regarding the impact of transistor aging on the IC behavior, to accurately distinguish between these two classes based on simple parametric measurements We demonstrate effectiveness of the proposed method using a set of actual fabricated devices which have been subjected to burn-in test, in order to mimic the impact of aging degradation over time, and we discuss the limitations and the potential extensions of this approach

http://www.utdallas.edu/~gxm112130/papers/dfts12.pdf

Parametric counterfeit IC detection via Support Vector Machines

We present a method for diagnosing local spot defects in analog circuits. The method aims to identify a subset of defects that are likely to have occurred and suggests to give them priority in a classical failure analysis. For this purpose, the method relies on a combination of multiclass classifiers that are trained using data from fault simulation. The method is demonstrated on an industrial large-scale case study. The device under consideration is a controller area network transceiver used in automobile systems. This device demands high-quality control due to the reliability requirements of the application wherein it is deployed. The diagnosis problem is discussed by taking into consideration the realities of this case study.

Diagnosis of Local Spot Defects in Analog Circuits

We introduce two statistical methods for identifying recycled integrated circuits (ICs) through the use of one-class classifiers and degradation curve sensitivity analysis. Both methods rely on statistically learning the parametric behavior of known new devices and using it as a reference point to determine whether a device under authentication has previously been used. The proposed methods are evaluated using actual measurements and simulation data from digital and analog devices, with experimental results confirming their effectiveness in distinguishing between new and aged ICs and their superiority over previously proposed methods.

http://www.utdallas.edu/~yiorgos.makris/papers/tcad15.pdf

Ke Huang

Papers

Counterfeit Integrated Circuits: A Rising Threat in the Global Semiconductor Supply Chain

Hardware Trojan Detection through Golden Chip-Free Statistical Side-Channel Fingerprinting

Parametric counterfeit IC detection via Support Vector Machines

Diagnosis of Local Spot Defects in Analog Circuits

Recycled IC Detection Based on Statistical Methods