Abstract: A device, such as a wet scrubber for cleaning a stream of gas by removing gaseous, liquid and solid impurities, has a gas inlet and a gas outlet. At the gas inlet there is a first structured packing. A spray-scrubber area with atomizing nozzles is provided above the first structured packing. A first demister is provided for separating larger drops above the spray-scrubber area. A woven fabric is above the first demister, and spray nozzles are arranged around this woven fabric to spray washing liquid in the direction of the gas stream onto the surface of the woven fabric. A final demister is located above the woven fabric and has a demister rinsing apparatus. The device may be used to dispose of industrial waste gases encountered in the semiconductor industry, particularly for the disposal of waste gases stemming from CVD processes in microelectronics production.

Abstract: Disclosure is related to a reconfigurable sorter and a method of sorting using the sorter. The reconfigurable sorting method is adapted to the sorter essentially consisting of multiple serially-connected comparison units. The each comparison unit includes two registers. The sorter is particularly a reconfigurable device according to the number of sorted numerals. According to the exemplary embodiment, an input mode is initiated firstly. Initial values are set to the registers. The numerals are sequentially inputted to the registers. At the input mode, the values in the registers may be shifted if necessary and mutually compared in every comparison unit. The values in the registers of every comparison unit may be swapped based on the comparison. At output mode, the numerals are outputted sequentially. The values in the registers are shifted and swapped until all numerals are completely outputted. The output appears the sorted numerals with low timing latency.

Abstract: Disclosure is related to a reconfigurable sorter and a method of sorting using the sorter. The reconfigurable sorting method is adapted to the sorter essentially consisting of multiple serially-connected comparison units. The each comparison unit includes two registers. The sorter is particularly a reconfigurable device according to the number of sorted numerals. According to the exemplary embodiment, an input mode is initiated firstly. Initial values are set to the registers. The numerals are sequentially inputted to the registers. At the input mode, the values in the registers may be shifted if necessary and mutually compared in every comparison unit. The values in the registers of every comparison unit may be swapped based on the comparison. At output mode, the numerals are outputted sequentially. The values in the registers are shifted and swapped until all numerals are completely outputted. The output appears the sorted numerals with low timing latency.

Abstract: A virtual production control system (VPCS), and a virtual production control method and a computer program product thereof are provided. At first, the VPCS processes historical work-in-process (WIP) information and a current shipping plan sent from a supplier side, thereby obtaining a plurality of sets of WIP input/output historical data and a goods output schedule. Then, the VPCS performs an integer programming (IP) method to find the latest output schedule in accordance to the current shipping plan; uses a genetic algorithm (GA) to fit the historical distributed-parameters; adopts a neural network (NN) method to predict the future distributed-parameters of production; and finally utilizes a Petri Nets to simulate and obtain a latest feasible input schedule and a latest feasible output schedule.

Abstract: A virtual production control system (VPCS), and a virtual production control method and a computer program product thereof are provided. At first, the VPCS processes historical work-in-process (WIP) information and a current shipping plan sent from a supplier side, thereby obtaining a plurality of sets of WIP input/output historical data and a goods output schedule. Then, the VPCS performs an integer programming (IP) method to find the latest output schedule in accordance to the current shipping plan; uses a genetic algorithm (GA) to fit the historical distributed-parameters; adopts a neural network (NN) method to predict the future distributed-parameters of production; and finally utilizes a Petri Nets to simulate and obtain a latest feasible input schedule and a latest feasible output schedule.