An Ensembled Anomaly Detector for Wafer Fault Detection

The production process of a wafer in the semiconductor industry consists of several phases such as a diffusion and associated defectivity test, parametric test, electrical wafer sort test, assembly and associated defectivity tests, final test, and burn-in. Among these, the fault detection phase is critical to maintain the low number and the impact of anomalies that eventually result in a yield loss. The understanding and discovery of the causes of yield detractors is a complex procedure of root-cause analysis. Many parameters are tracked for fault detection, including pressure, voltage, power, or valve status. In the majority of the cases, a fault is due to a combination of two or more parameters, whose values apparently stay within the designed and checked control limits. In this work, we propose an ensembled anomaly detector which combines together univariate and multivariate analyses of the fault detection tracked parameters. The ensemble is based on three proposed and compared balancing strategies. The experimental phase is conducted on two real datasets that have been gathered in the semiconductor industry and made publicly available. The experimental validation, also conducted to compare our proposal with other traditional anomaly detection techniques, is promising in detecting anomalies retaining high recall with a low number of false alarms.

Automated Test Assembly in R: The eatATA Package

Combining items from an item pool into test forms (test assembly) is a frequent task in psychological and educational testing. Although efficient methods for automated test assembly exist, these are often unknown or unavailable to practitioners. In this paper we present the R package eatATA, which allows using several mixed-integer programming solvers for automated test assembly in R. We describe the general functionality and the common work flow of eatATA using a minimal example. We also provide four more elaborate use cases of automated test assembly: (a) The assembly of multiple test forms for a pilot study; (b) the assembly of blocks of items for a multiple matrix booklet design in the context of a large-scale assessment; (c) the assembly of two linear test forms for individual diagnostic purposes; (d) the assembly of multi-stage testing modules for individual diagnostic purposes. All use cases are accompanied with example item pools and commented R code.

A Short Measure of Acceptability of Intimate Partner Violence Against Women: Development and Validation of the A-IPVAW-8 Scale

Attitudes of acceptability of intimate partner violence against women (IPVAW) are considered one of the main risk factors of this type of violence. The aim of this study is to develop and validate a short version of the acceptability of IPVAW scale, the A-IPVAW-8, for large scale studies where space and time are limited. A panel of experts were asked to assess item content validity. Two samples were recruited to assemble an 8-item short version of the scale using automated test assembly, and to reassess the psychometric properties of the A-IPVAW-8 in an independent sample. Results showed that the A-IPVAW-8 had adequate internal consistency (α = .72-.76, ω = .73-.81), a stable one-factor latent structure (comparative fit index [CFI] = 0.94, Tucker–Lewis index = 0.92, root mean square error of approximation = 0.077), validity evidences based on its relationships to other variables in both samples, and was also invariant across gender (ΔCFI < |0.02|). This study provides a short, easy-to-use tool to evaluate attitudes of acceptability of IPVAW for large scale studies.

Test Assembly for Cognitive Diagnosis Using Mixed-Integer Linear Programming

One purpose of cognitive diagnostic model (CDM) is designed to make inferences about unobserved latent classes based on observed item responses. A heuristic for test construction based on the CDM information index (CDI) proposed by Henson and Douglas (2005) has a far-reaching impact, but there are still many shortcomings. He and other researchers had also proposed new methods to improve or overcome the inherent shortcomings of the CDI test assembly method. In this study, one test assembly method of maximizing the minimum inter-class distance is proposed by using mixed-integer linear programming, which aims to overcome the shortcomings that the CDI method is limited to summarize the discriminating power of each item into a single CDI index while neglecting the discriminating power for each pair of latent classes. The simulation results show that compared with the CDI test assembly and random test assembly, the new test assembly method performs well and has the highest accuracy rate in terms of pattern and attributes correct classification rates. Although the accuracy rate of the new method is not very high under item constraints, it is still higher than the CDI test assembly with the same constraints.

A Short Form of the Chinese Version of the Weinstein Noise Sensitivity Scale through Optimal Test Assembly

This study developed a short form of the traditional Chinese version of the Weinstein Noise Sensitivity Scale (WNSS) through optimal test assembly (OTA). A total of 1069 Chinese adults (64.8% female) completed the territory-wide cross-sectional study. We first removed Items 12 and 5 which had negative factor loading and gender-related differential item functioning (DIF), respectively. The optimal length was then determined as the minimal one that reasonably resembled the reliability and validity of the scale without DIF items. OTA identified an 8-item WNSS (WNSS-8) which retained 67.2% of the test information of the original 21-item scale and had a Cronbach’s alpha of 0.83. It also showed significant correlations of 0.272 and −0.115 with the neuroticism and extraversion scales of Chinese NEO-Five Factor Inventory, respectively. Adequate model fit of the WNSS-8 was demonstrated by the confirmatory factor analysis. The Chinese WNSS-8 can be used to assess noise sensitivity without compromising reliability and validity.