Abstract-This paper improves upon the state of the art in the testing of intraword coupling faults (CFs) in word-oriented memories. It first presents a complete set of fault models for intraword CFs. Then, it establishes the data background sequence and tests for each intraword CF, as well as a test with complete fault coverage of the targeted faults. All introduced tests will be evaluated industrially, together with the most well-known memory tests. The tests will be applied to big arrays with an interleaved bit-organization as well as to small arrays with an adjacent bit-organization in order to investigate the influence of the memory organization on the intraword CFs. The test results show that the intraword CFs are also significantly important for interleaved memories, even when the cells within a single cell are not physically adjacent. This is due to coupling between the adjacent bit lines and word lines running across the memory array. The paper concludes that intraword CFs should be considered for any serious test purpose or leave substantial defects undetected, especially when considering a high-volume production and a very low defect-per-million (DPM) level.Index Terms-Bit-oriented memories (BOMs), data backgrounds (DBs), fault models (FMs), memory tests, word-oriented memories (WOMs).
Using author provided tags to predict tags for a new document often results in the overgeneration of tags. In the case where the author doesn't provide any tags, our documents face the severe under-tagging issue. In this paper, we present a method to generate a universal set of tags that can be applied widely to a large document corpus. Using the IBM Watson's NLU service, first, we collect keywords/phrases that we call "complex document tags" from 8,854 popular reports in the corpus. We apply LDA model over these complex document tags to generate a set of 765 unique "simple tags". In applying the tags to a corpus of documents, we run each document through the IBM Watson NLU and apply appropriate simple tags. Using only 765 simple tags, our method allows us to tag 87,397 out of 88,583 total documents in the corpus with at least one tag. About 92.1% of the 87,397 documents are also determined to be sufficiently-tagged. In the end, we discuss the performance of our method and its limitations.
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