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 present disclosure is directed to an apparatus and method for producing and comparing signals from various points in a MEMS device. By producing signals which should be of substantial identical characteristics, deviations from the situation where the signals are of identical characteristics can be used to identify various types of asymmetry which are otherwise difficult to detect. In one embodiment, the MEMS device is comprised of a plurality of fixed beams arranged symmetrically and a plurality of movable beams arranged symmetrically. A first sensor is formed by certain of the fixed and movable beams while a second sensor, electrically isolated from said first sensor, is formed by at least certain other of the fixed and movable beams. The first and second sensors are located within the MEMS device so as to produce signals of substantially identical characteristics. A circuit is responsive to the first and second sensors for comparing the signals produced by the first and second sensors. In addition to the apparatus, methods of performing a self test are also disclosed, which may be performed in real time.

Built-in self test of MEMS

We describe a rigorous ATPG-like methodology for validating the branch prediction mechanism of the PowerPC604 which can be easily generalized and made applicable to other processors. Test sequences based on finite state machine (FSM) testing are derived from small FSM-like models of the branch prediction mechanism. These sequences are translated into PowerPC instruction sequences. Simulation results show that 100% coverage of the targeted functionality is achieved using a very small number of simulation cycles. Simulation of some real programs against the same targeted functionality produces coverages that range between 34% and 75% with four orders of magnitude more cycles. We also use mutation analysis to modify some functionality of the behavioral model to further illustrate the effectiveness of our generated sequence. Simulation results show that all 54 mutants in the branch prediction functionality can be detected by measuring transition coverage.

Superscalar processor validation at the microarchitecture level

We propose a methodology that employs boolean minimization and optimized test covering to identify redundant tests of a mixed-signal circuit from its pass-fail (binary) test data. This methodology is applied to two in-production circuits, a high-speed serializer/deserializer (HSS) and a phase-locked loop (PLL). Application of the methodology to over 38, 000 failing HSS circuits demonstrate that only 0.016% of them are mispredicted when three of nine high- voltage HSS tests are eliminated. Similarly, analysis of 22, 000 failing PLL circuits results in an error of 0.032% when 11 out of the 36 PLL tests are eliminated. Assuming 90% yield, these misprediction levels for the HSS and the PLL designs are equivalent to 16 and 32 DPM, respectively. The cost savings from eliminating the redundant tests in the HSS and the PLL designs at 90% yield are however estimated to be 21.9% and 30.9%, respectively.

Test Compaction for Mixed-Signal Circuits Using Pass-Fail Test Data

Wafer-level testing of surface-micromachined sensors provides new challenges to the test community. Currently, there is no method available for performing direct measurements to assess faulty micromechanical structures. Most commercial methods use electrical measurements to deduce the physical source of failures in the micromechanical structure. As a result, the process of identifying various failure modes (electrical measurements) and accurately mapping them to the underlying physical failure mechanisms of the mechanical sensor becomes highly complex. Several sources of failures that include particulates and stiction complicate the situation even more. Here, we provide a case study of the Analog Devices ADXL75 Accelerometer. One failure category called stuck/tipped beams is investigated and a methodology is developed to uniquely distinguish failures either caused by stuck or tipped beams. The proposed method is easy to implement and the information obtained can be critical for yield improvement.

Failure modes for stiction in surface-micromachined MEMS

N-detect test has been shown to have a higher likelihood for detecting defects. However, traditional definitions of N-detect test do not necessarily exploit the localized characteristics of defects. In physically-aware N-detect test, the objective is to ensure that the N tests establish N different logical states on the signal lines that are in the physical neighborhood surrounding the targeted fault site. We present a test selection procedure for creating a physically- aware N-detect test set that satisfies a user-provided constraint on test-set size. Results produced for an industrial test chip demonstrate the effectiveness and practicability of our pattern selection approach. Specifically, we show that we can virtually detect the same number of faults 10 or more times as a traditional 10-detect test set and increase the number of neighborhood states and the number of faults with 10 or more states by 18.0 and 4.7%, respectively, without increasing the number of tests over a traditional 10-detect test set.

/pdf/physically-aware-n-detect-test-pattern-selection-2pna2g7nh9.pdf

R.D. Blanton

Papers

Built-in self test of MEMS

Superscalar processor validation at the microarchitecture level

Test Compaction for Mixed-Signal Circuits Using Pass-Fail Test Data

Failure modes for stiction in surface-micromachined MEMS

Physically-aware N-detect test pattern selection