The conventional wisdom holds that CMOS devices cannot be scaled much further from where they are today because of several device physics limitations such as the large tunneling current in very thin gate dielectrics. ...The conventional wisdom holds that CMOS devices cannot be scaled much further from where they are today because of several device physics limitations such as the large tunneling current in very thin gate dielectrics. It is shown that alternative device structures can allow CMOS transistors to scale by another 20 times. That is as large a factor of scaling as what the semiconductor industry accomplished in the past 25 years. There will be many opportunities and challenges in finding novel device structures and new processing techniques, and in understanding the physics of future devices.展开更多
The hybrid CMOS molecular （CMOL） circuit, which combines complementary metal-oxide- semiconductor （CMOS） components with nanoscale wires and switches, can exhibit significantly improved performance. In CMOL circui...The hybrid CMOS molecular （CMOL） circuit, which combines complementary metal-oxide- semiconductor （CMOS） components with nanoscale wires and switches, can exhibit significantly improved performance. In CMOL circuits, the nanodevices, which are called cells, should be placed appropriately and are connected by nanowires. The cells should be connected such that they follow the shortest path. This paper presents an efficient method of cell allocation in CMOL circuits with the hybrid CMOS/nanodevice structure; the method is based on a cultural algorithm with chaotic behavior. The optimal model of cell allocation is derived, and the coding of an individual represent- ing a cell allocation is described. Then the cultural algorithm with chaotic behavior is designed to solve the optimal model. The cultural algorithm consists of a population space, a belief space, and a protocol that describes how knowledge is exchanged between the population and belief spaces. In this paper, the evolutionary processes of the population space employ a genetic algorithm in which three populations undergo parallel evolution. The evolutionary processes of the belief space use a chaotic ant colony algorithm. Extensive experiments on cell allocation in benchmark circuits showed that a low area usage can be obtained using the proposed method, and the computation time can be reduced greatly compared to that of a conventional genetic algorithm.展开更多
基金This research is partially supported by the DARPA Advanced Microelectronics Program, SRC Front EndProcess Project, and NSF. This paper contains a review of UTB and FinFET research published by D. Hisamoto, Y-K. Choi, X. Huang, W-C. Lee, C. Kuo, L. Chan
文摘The conventional wisdom holds that CMOS devices cannot be scaled much further from where they are today because of several device physics limitations such as the large tunneling current in very thin gate dielectrics. It is shown that alternative device structures can allow CMOS transistors to scale by another 20 times. That is as large a factor of scaling as what the semiconductor industry accomplished in the past 25 years. There will be many opportunities and challenges in finding novel device structures and new processing techniques, and in understanding the physics of future devices.
文摘The hybrid CMOS molecular （CMOL） circuit, which combines complementary metal-oxide- semiconductor （CMOS） components with nanoscale wires and switches, can exhibit significantly improved performance. In CMOL circuits, the nanodevices, which are called cells, should be placed appropriately and are connected by nanowires. The cells should be connected such that they follow the shortest path. This paper presents an efficient method of cell allocation in CMOL circuits with the hybrid CMOS/nanodevice structure; the method is based on a cultural algorithm with chaotic behavior. The optimal model of cell allocation is derived, and the coding of an individual represent- ing a cell allocation is described. Then the cultural algorithm with chaotic behavior is designed to solve the optimal model. The cultural algorithm consists of a population space, a belief space, and a protocol that describes how knowledge is exchanged between the population and belief spaces. In this paper, the evolutionary processes of the population space employ a genetic algorithm in which three populations undergo parallel evolution. The evolutionary processes of the belief space use a chaotic ant colony algorithm. Extensive experiments on cell allocation in benchmark circuits showed that a low area usage can be obtained using the proposed method, and the computation time can be reduced greatly compared to that of a conventional genetic algorithm.
