University of California

Lin H Yang

Leon O Chua

Ching Yao Fong

Meichun Qian

One embodiment of the present invention provides a switching device that can vary a spin-polarized current based on an input signal. The switching device comprises a first conducting region, a second conducting region, and a half-metal region interposed between the first conducting region and the second conducting region. The half-metal region comprises a material which, at the intrinsic Fermi level, has substantially zero available electronic states in a minority spin channel. Changing the voltage of the half-metal region with respect to the first conducting region moves its Fermi level with respect to the electron energy bands of the first conducting region, which changes the number of available electronic states in the majority spin channel, and in doing so, changes the majority-spin polarized current passing through the switching device.

Switching devices based on half-metals

A novel class of information-processing systems called a cellular neural network is discussed. Like a neural network, it is a large-scale nonlinear analog circuit which processes signals in real time. Like cellular automata, it is made of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through its nearest neighbors. Each cell is made of a linear capacitor, a nonlinear voltage-controlled current source, and a few resistive linear circuit elements. Cellular neural networks share the best features of both worlds; its continuous time feature allows real-time signal processing found within the digital domain and its local interconnection feature makes it tailor made for VLSI implementation. Cellular neural networks are uniquely suited for high-speed parallel signal processing.

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Pleasanton, CA, United States of America

Lin H Yang