Abstract: A control box for charging includes: a first connector connected to a supply cable having a plug, which is connected to an external power source, at a first end, and having a plurality of connection pins; and a control board having a first switch mounted therein and configured to control the first switch in a first state such that an input signal is transmitted to an output terminal of the control box when power is supplied in a first mode in which a signal is input from the first connector. In particular, the control board is configured to control the first switch in a second state to transmit and receive signals to and from a vehicle connected to the output terminal when power is supplied in a second mode in which a signal is not input from the first connector.

Abstract: The present disclosure relates to a cleaning agent composition for a substrate for a semiconductor device and a method for cleaning a substrate for a semiconductor device using the same. The cleaning agent composition contains a silicon-based compound represented by Formula 1 and an aprotic organic solvent with a dielectric constant of 10 or less, which can form a surface protective film capable of preventing collapse of the pattern even in a wet cleaning process of fine patterns with high aspect ratios, thereby providing a method for manufacturing a semiconductor device with an improved semiconductor manufacturing yield.

Abstract: A display device comprising: first and second pixels; a first data line connected to the first pixel and configured to have data voltages applied thereto; and a second data line connected to the second pixel, the second data line being adjacent to the first data line, and configured to have the data voltages applied thereto, wherein the first data line includes a 1A-th data line which is in a first data layer, and the second data line includes a 2B-th data line which is in a second data layer different from the first data layer.

Abstract: A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.

Abstract: A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.

Abstract: A semiconductor design automation system comprises a simulator configured to generate simulation data, a recovery module configured to correct a sampling error of the simulation data to generate recovery simulation data, a hardware data module configured to generate real data, a preprocessing module configured to preprocess the real data to generate preprocessed real data, a database configured to store the recovery simulation data and the preprocessed real data, a first graphic user interface including an automatic simulation generator configured to generate a machine learning model of the recovery simulation data and the preprocessed real data and generate predicted real data therefrom, and a second graphic user interface including a visualization unit configured to generate a visualized virtualization process result from the machine learning model.