Efficient Test Set Modification for Capture Power Reduction

The occurrence of high switching activity when the response to a test vector is captured by flipflops in scan testing may cause excessive IR drop, resulting in significant test-induced yield loss. This paper addresses the problem with a novel method based on test set modification, featuring (1) a new constrained X-identification technique that turns a properly selected set of bits in a fullyspecified test set into X-bits without fault coverage loss, and (2) a new LCP (low capture power) X-filling technique that optimally assigns 0’s and 1’s to the X-bits for the purpose of reducing the switching activity of the resulting test set in capture mode. This method can be readily applied in any test generation flow for capture power reduction without any impact on area, timing, test set size, and fault coverage

Effective Launch-to-Capture Power Reduction for LOS Scheme with Adjacent-Probability-Based X-Filling

It has become necessary to reduce power during LSI testing. Particularly, during at-speed testing, excessive power consumed during the Launch-To-Capture (LTC) cycle causes serious issues that may lead to the overkill of defect-free logic ICs. Many successful test generation approaches to reduce IR-drop and/or power supply noise during LTC for the launch-off capture (LOC) scheme have previously been proposed, and several of X-filling techniques have proven especially effective. With X-filling in the launch-off shift (LOS) scheme, however, adjacent-fill (which was originally proposed for shift-in power reduction) is used frequently. In this work, we propose a novel X-filling technique for the LOS scheme, called Adjacent-Probability-based X-Filling (AP-fill), which can reduce more LTC power than adjacent-fill. We incorporate AP-fill into a post-ATPG test modification flow consisting of test relaxation and X-filling in order to avoid the fault coverage loss and the test vector count inflation. Experimental results for larger ITC\u2799 circuits show that the proposed AP-fill technique can achieve a higher power reduction ratio than 0-fill, 1-fill, and adjacent-fill.2011 Asian Test Symposium, 20-23 November 2011, New Delhi, Indi

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background. Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling

A Scan-Out Power Reduction Method for Multi-Cycle BIST

High test power in logic BIST is a serious problem not only for production test, but also for board test, system debug or field test. Many low power BIST approaches that focus on scan-shift power or capture power have been proposed. However, it is known that a half of scan-shift power is compensated by test responses, which is difficult to control in those approaches. This paper proposes a novel approach that directly reduces scan-out power by modifying some flip-flops\u27 values in scan chains at the last capture. Experimental results show that the proposed method reduces scan-out power up to 30% with little loss of test coverage.2012 IEEE 21st Asian Test Symposium, 19-22 Nov. 2012, Niigata, Japa

Low-Capture-Power Test Generation for Scan-Based At-Speed Testing

Scan-based at-speed testing is a key technology to guarantee timing-related test quality in the deep submicron era. However, its applicability is being severely challenged since significant yield loss may occur from circuit malfunction due to excessive IR drop caused by high power dissipation when a test response is captured. This paper addresses this critical problem with a novel low-capture-power X-filling method of assigning 0\u27s and 1\u27s to unspecified (X) bits in a test cube obtained during ATPG. This method reduces the circuit switching activity in capture mode and can be easily incorporated into any test generation flow to achieve capture power reduction without any area, timing, or fault coverage impact. Test vectors generated with this practical method greatly improve the applicability of scan-based at-speed testing by reducing the risk of test yield lossIEEE International Conference on Test, 2005, 8 November 2005, Austin, TX, US

BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data

This paper presents a novel BigEAR big data framework that employs psychological audio processing chain (PAPC) to process smartphone-based acoustic big data collected when the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer from various activities such as laughing, singing, crying, arguing, and sighing. These annotations are based on ground truth relevant for psychologists who intend to monitor/infer the social context of individuals coping with breast cancer. We pursued a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. In the state-of-the-art methods, psychologists and their team have to hear the audio recordings for making these inferences by subjective evaluations that not only are time-consuming and costly, but also demand manual data coding for thousands of audio files. The BigEAR framework automates the audio analysis. We computed the accuracy of BigEAR with respect to the ground truth obtained from a human rater. Our approach yielded overall average accuracy of 88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27, 2016, Washington DC, US