Diagnosis of Multiple Scan-Chain Faults in the Presence of System Logic Defects

We present a combined hardware-software based approach to scan-chain diagnosis, when the outcome of a test may be affected by system faults occurring in the logic outside of the scan chain. For the hardware component we adopt the double-tree scan (DTS) chain architecture, which has previously been shown to be effective in reducing power, volume, and application time of tests for stuck-at and delay faults. We develop a version of flush test which can resolve a multiple fault in a DTS chain to a small number of suspect candidates. Further resolution to a unique multiple fault is enabled by the software component comprising of fault simulation and analysis of the response of the circuit to test patterns produced by ATPG. Experimental results on benchmark circuits show that near-perfect scan-chain diagnosis for multiple faults is possible even when a large number of random system faults are injected in the circuit

Constructing A Flexible Likelihood Function For Spectroscopic Inference

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically address the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line "outliers." By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high resolution $V$-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate resolution $K$-band spectrum of Gliese 51, an M5 field dwarf.Comment: Accepted to ApJ. Incorporated referees' comments. New figures 1, 8, 10, 12, and 14. Supplemental website: http://iancze.github.io/Starfish