There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

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

Micromachining and micro-electromechanical system (MEMS) technologies can be used to produce complex structures, devices and systems on the scale of micrometers. Initially micromachining techniques were borrowed directly from the integrated circuit (IC) industry, but now many unique MEMS-specific micromachining processes are being developed. In MEMS, a wide variety of transduction mechanisms can be used to convert real-world signals from one form of energy to another, thereby enabling many different microsensors, microactuators and microsystems. Despite only partial standardization and a maturing MEMS CAD technology foundation, complex and sophisticated MEMS are being produced. The integration of ICs with MEMS can improve performance, but at the price of higher development costs, greater complexity and a longer development time. A growing appreciation for the potential impact of MEMS has prompted many efforts to commercialize a wide variety of novel MEMS products. In addition, MEMS are well suited for the needs of space exploration and thus will play an increasingly large role in future missions to the space station, Mars and beyond. (Some figures in this article are in colour only in the electronic version)

https://iopscience.iop.org/article/10.1088/0964-1726/10/6/301/pdf

Microelectromechanical systems (MEMS):fabrication, design and applications

The input pattern (IP) fault model is a functional fault model that allows for both complete and partial functional verification of every circuit primitive, independent of the design level. Here, we formalize the model and provide a method for analyzing IP faults using single stuck-line (SSL) based tools.

The input pattern fault model and its application

A Multi-Stage Approach to Fault Identification Using Fault Tuples

An ensemble of machine learning classifiers usually improves generalization performance and is useful for many applications. However, the extra memory storage and computational cost incurred from the combined models often limits their potential applications. In this article, we propose a new ensemble reduction method called CANOPY that significantly reduces memory storage and computations. CANOPY uses a technique from logic minimization for digital circuits to select and combine particular classification models from an initial pool in the form of a Boolean function, through which the reduced ensemble performs classification. Experiments on 20 UCI datasets demonstrate that CANOPY either outperforms or is very competitive with the initial ensemble and one state-of-the-art ensemble reduction method in terms of generalization error, and is superior to all existing reduction methods surveyed for identifying the smallest numbers of models in the reduced ensembles.

Ensemble Reduction via Logic Minimization

A method and apparatus for identifying suspect layout features from a plurality of layout features of an integrated circuit (IC) layout. A plurality of snippet images is generated, each of which depicts at least a portion of a suspect layout feature which is different from suspect layout features depicted in others of the plurality of snippet images. The suspect layout features are determined based on the diagnosis of a plurality of defective ICs manufactured in accordance with the IC layout. A plurality of clusters is generated, and each of the clusters contains a group of the plurality of snippet images based on similarities between the snippet images.

Method and system for systematic defect identification

We study the failure mechanisms of a microresonator. A MEMs process simulation tool is used to discover the full spectrum of defective structures caused by particulate contaminations introduced during manufacturing. Based on process simulation results, defect categorization is performed. Mechanical and electrical simulation are then used to quantify the misbehaviors associated with the categories of defective structures. The analysis reveals that particulates can cause both parametric and catastrophic changes in behavior. Also, we found types of defects that do not affect mechanical behavior but have a significant impact on electrical behavior.

/pdf/inductive-fault-analysis-of-a-microresonator-2iiehrvnmt.pdf

R.D. Blanton

Papers

The input pattern fault model and its application

A Multi-Stage Approach to Fault Identification Using Fault Tuples

Ensemble Reduction via Logic Minimization

Method and system for systematic defect identification

Inductive Fault Analysis of a Microresonator