Title :
Combining Neural Networks and Genetic Algorithms for Optimizing the Parameter Design of Inter-Metal Dielectric Layer
Author :
Chou, Chia-Jen ; Yu, Fong-Jung ; Su, Chao-Ton
Author_Institution :
Dept. of Ind. Eng. & Eng. Manage., Nat. Tsing Hua Univ., Hsinchu
Abstract :
Integrated circuits generally involve many layers of metallization as semiconductor devices require different functions; otherwise, the devices´ density increases. The inter-metal dielectric (IMD) is deposited between metal layers to provide isolated capability to the device and separate the different metal layers, which are not necessary in conducting electricity. A good isolated capability will help the devices become more reliable and stable. The key problem in IMD layer is the occurrence of voids, which lead to electric leakage and cause wafer scrape. To overcome the void problem in the IMD process is difficult due to its complicated input-response relationship. In this study, the authors combined neural networks, genetic algorithms (GAs), and desirability function to optimize the IMD process. The implementation of the proposed approach was carried out in a semiconductor manufacturing company in Taiwan, and the results illustrated the practicability of the said approach.
Keywords :
circuit analysis computing; genetic algorithms; integrated circuit metallisation; neural nets; Taiwan; electric leakage; genetic algorithm; integrated circuit; inter-metal dielectric; neural network; semiconductor device; semiconductor manufacturing company; Algorithm design and analysis; Design engineering; Design optimization; Dielectric devices; Genetic algorithms; Genetic engineering; Manufacturing processes; Metals industry; Neural networks; Neurons;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
DOI :
10.1109/WiCom.2008.1853