University of California

Other

Industrial Cooperation Foundation Chonbuk National University

Leon O Chua

Tamas Roska

Frank S Werblin

Lin H Yang

Hyongsuk Kim

Tao Yang

Isutomu Sugawara

The present invention relates to a memristor, and more particularly, to a method of implementing a memristor-based multilevel memory using a reference resistor array and a write-in circuit and a read-out/restoration circuit for the memristor-based multilevel memory, in which a memristor can be used as a multilevel memory. In the present invention, a reference resistance value is written in a selected memristor of a memristor array by applying repeatedly the current pulses of which widths are proportional to the difference between the resistances of the selected memristor and the selected node of the reference resistor array.

Method of implementing memristor-based multilevel memory using reference resistor array

In the present invention, the structure, principle and framework of chaotic digital code-division multiple access ((CD),MA) communication systems are presented. Unlike the existing CDMA systems, (CD),MA systems use continuous pseudo-random time series to spread the spectrum of message signal and the spread signal is then directly sent through channel to the receiver. In this sense, the carrier used in (CD),MA is a continuous pseudo-random signal instead of a single tone as used in CDMA. The statistical properties of the noise-like carriers are given. In a (CD),MA system, every mobile station has the same structure and parameters, only different initial conditions are assigned to different mobile stations. Instead of synchronizing two binary pseudo-random sequences as in CDMA systems, an impulsive control scheme is used to synchronize two chaotic systems in (CD),MA. Simulation results show that the channel capacity of (CD),MA is at least twice as large than that of CDMA.

Chaotic digital code-division multiple access (CDMA) communication systems

The main design components underlying the implementation of physiologically faithful retina and other topographic sensory organ models on Cellular Neural Network (CNN) universal chips is discussed. If the various retinas are implemented on a CNN universal chip, in a programmable way, it can be called a 'CNN bionic eye', a device capable of performing a broad range of image processing functions similar to those performed by biological retinas. The CNN universal machine has the special properties that it is 1) programmable and 2) includes local memory. Programming is stored in analog and logical form (the analogic program) generated by an analogic programming and control unit, so the functions of the CNN universal machine can be modified as a function of complex internal and external constraints. Further, several CNN bionic eyes and other topographic sensory modalities can be combined on a single CNN universal chip, and, for more complex sensory tasks, the necessary physical microsensors to provide the input signals can be implemented on the chip, in most instances.

CNN programamble topographic sensory device

This invention has 3 parts. Part 1 proposes a new CNN universal chip architecture with analog stored programs and time-multiplex templates. This breakthrough replaces hundreds of dedicated CNN chips with a single programmable, real-time VLSI chip with comparable significance to the invention of the microprocessor which gave birth to the personal computer industry. Part 2 proposes a unique wireless non-optical method for outputting information from the CNN analog array via electromagnetic waves generated by nonlinear oscillations and chaos. Part 3 combines a set of analog, or digitally emulated, CNN universal chips to design a CNN array supercomputer capable of solving nonlinear partial differential equations (e.g., wave type, Navier-Stokes-type, etc.) at a fraction of the time or cost required by current digital supercomputers. Indeed, 'programmable physics' or 'programmable bionics' can be implemented.

Reprogrammable CNN and supercomputer

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.

Cellular neural network

Apparatus and method for use with a cathode ray oscilloscope for displaying from any desired perspective a multi-dimensional surface evolving in time multi electrical signal are introduced to attachment to related electrical signal-carrying means, a multiplicity of two-dimensional rotators. For the number N of signals and therefore of N dimensions there are

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Berkeley, CA, United States of America

Leon O Chua