Grey Situation Decision-making Algorithm to Optimize Silicon Wafer Slicing

The slicing of Silicon wafer is a complex manufacturing process in producing the raw materials for electronic chips and requires the efforts to effectively monitor the stability in production line and ensure the quality for the products composed of different shapes and materials. Human decision failure and other analytical errors are the most common source of management problems in such manufacturing stage. This paper presents a case regarding the silicon wafer manufacturing to examine the response to quality errors. The study has adopted the approach of grey situation decision-making algorithm for problem detection that suggests a technique to attain the quality control and reduce potential costs in production

Persistent currents in a graphene ring with armchair edges

A graphene nano-ribbon with armchair edges is known to have no edge state. However, if the nano-ribbon is in the quantum spin Hall (QSH) state, then there must be helical edge states. By folding a graphene ribbon to a ring and threading it by a magnetic flux, we study the persistent charge and spin currents in the tight-binding limit. It is found that, for a broad ribbon, the edge spin current approaches a finite value independent of the radius of the ring. For a narrow ribbon, inter-edge coupling between the edge states could open the Dirac gap and reduce the overall persistent currents. Furthermore, by enhancing the Rashba coupling, we find that the persistent spin current gradually reduces to zero at a critical value, beyond which the graphene is no longer a QSH insulator

Personalized Audio Quality Preference Prediction

This paper proposes to use both audio input and subject information to predict the personalized preference of two audio segments with the same content in different qualities. A siamese network is used to compare the inputs and predict the preference. Several different structures for each side of the siamese network are investigated, and an LDNet with PANNs' CNN6 as the encoder and a multi-layer perceptron block as the decoder outperforms a baseline model using only audio input the most, where the overall accuracy grows from 77.56% to 78.04%. Experimental results also show that using all the subject information, including age, gender, and the specifications of headphones or earphones, is more effective than using only a part of them