Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System

With the development of artificial intelligence, the artificial neural networks (ANN) are widely used in the control, decision‐making and prediction of complex discrete event manufacturing systems. Wafer fabrication is one of the most complicated and high competence manufacturing phases. The production scheduling and yield prediction are two critical issues in the operation of semiconductor wafer fabrication system (SWFS). This chapter proposed two fuzzy neural networks for the production rescheduling strategy decision and the die yield prediction. Firstly, a fuzzy neural network (FNN)‐based rescheduling decision model is implemented, which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to the current system disturbances. The experimental results demonstrate the effectiveness of proposed FNN‐based rescheduling decision mechanism approach over the alternatives (back‐propagation neural network and Multivariate regression). Secondly, a novel fuzzy neural network‐based yield prediction model is proposed to improve prediction accuracy of die yield in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy

Selective Refinement Network for High Performance Face Detection

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author: Shifeng Zhang ([email protected]

Deep Denitrification of Domestic Sewage by Sulfur-based Mixotrophic Denitrification Filter

As a result of relevant policies and regulations, most wastewater treatment plants are faced with upgrading to further improve the level of effluent targets. To this end, this paper conducts an experimental study on deep denitrification in the sulfur Oyster shells mixotrophic nitrification filter process using sulfur as filler. During the experiments, when the water temperature in the mixotrophic pool was 15 °C, the nitrogen load of the inflow was 7.3 × 10−3kg/m3·d and HRT equaled to 3.5 h, the average TN concentration in the effluent is 3.42 mg/L, and the TN removal rate reaches 54.49%, which can stably meet the core control area standard in the "Discharge Standard of Water Pollutants in Daqing River Basin" (DB13/2795-2018) and are the best operating parameters during the experimental period. The test results show that oyster shells can provide a large amount of alkalinity, alleviating the pH drop in the water column and effectively mitigating the acidification of the water column. Based on experimental calculations, without considering the loss of packing material, the operating cost of the sulphur-mixed denitrification filter process is reduced by $ 0.191 per tonne of water compared with the existing deep treatment unit in the WWTP. The above results show that the sulphur mixer denitrification filter has the ability to degrade the secondary effluent TN in depth, which provides some experimental basis for the sulphur mixer denitrification filter to be used as a deep treatment unit