Self-assembling of nanocrystals involves organization of nanocrystals encapsulated by protective compact organic molecules into a crystalline material. The adsorbed molecules not only serve as the protection layer for the nanocrystals but also provide the dominant cohesive interactions (or "bonding") sustaining the nanocrystal superlattices. The length of the adsorbed molecules is a controllable parameter, making the ratio of particle size to interparticle distance an adjustable parameter that sensitively tunes the interparticle interaction/coupling and resulting collective properties. In this paper, bundling and interdigitation of thiolate molecules adsorbed on Ag nanocrystals are observed using the chemical imaging technique in energy-filtered transmission electron microscopy (EF-TEM) at a resolution of ∼2 nm. In these orientationally ordered, self-assembled Ag−nanocrystal superlattices, the bundling of the adsorbed molecules on the nanocrystal surfaces is the fundamental structural principle. A model consistent with the nanocrystal's morphology and the interdigitation of the adsorbed thiolates is proposed.
Diagnosis of functional failures is critical for locating manufacturing defects, increasing yield, and reducing field returns. It is important to narrow down the defective module in a failed component during board-level diagnosis. In this paper, a generic fault-diagnosis method based on an error-flow dictionary is presented to identify the root cause of functional failures on a chip or board. Error propagation mimics actual dataflow in a circuit, thus it reflects the native (functional) mode of circuit operation. In contrast to conventional fault syndromes, error flow includes the failure information in terms of circuit functionality, which significantly facilitates the diagnosis of functional failures. In the proposed diagnosis procedure, error flow is first learned from a good circuit by intentionally inserting faults, and then the root cause of a failing circuit is determined by comparing the similarity between the pre-learned error flow and the error flow observed from the failing circuit. The similarity of two error flows is evaluated based on the length of the longest common subsequence in string matching. Results for an open-source RISC SoC and an industrial communication circuit highlight the effectiveness of the proposed method.
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTDynamic light scattering study of internal motions of polymer coils in dilute solutionBenjamin Chu, Zhulun Wang, and Jiqun YuCite this: Macromolecules 1991, 24, 26, 6832–6838Publication Date (Print):December 1, 1991Publication History Published online1 May 2002Published inissue 1 December 1991https://pubs.acs.org/doi/10.1021/ma00026a005https://doi.org/10.1021/ma00026a005research-articleACS PublicationsRequest reuse permissionsArticle Views619Altmetric-Citations87LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts
We present a built-in self-test (BIST) approach for full-scan designs that extracts the most frequently occurring sequences from deterministic test patterns. The extracted sequences are stored on-chip, and are used during test application. Three sets of test patterns are applied to the circuit under test during a BIST test session; these include pseudorandom patterns, semirandom patterns, and deterministic patterns. The semirandom patterns are generated based on the stored sequences and they are more likely to detect hard-to-detect faults than pseudorandom patterns. The deterministic patterns are encoded using either the stored sequences or the LFSR reseeding technique to reduce test data volume. We use the cluster analysis technique for sequence extraction to reduce the amount of data to be stored. Experimental results for the ISCAS-89 benchmark circuits show that the proposed approach often requires less on-chip storage and test data volume than other recent BIST methods.