Transconductance Operational Amplifier Circuit and Cellular Neural Network

Abstract

The application discloses a transconductance operation amplification circuit which comprises a ground connection end, a power source voltage end, a processing module, a current bias module and a mirror image module. The processing module comprises a first input end, a second input end and a current output end. The first input end is used for inputting adjustable fixed voltage Vi1, the second input end is used for inputting voltage Vi2, the current output end is used for outputting an amplified current Io1, and the processing module is used for determining the current Io1 according to difference between the fixed voltage Vi1 and the voltage Vi2. The current bias module is used for providing a bias current for the processing module, and the mirror image module is used for outputting an image current Io2 according to the current Io1; each of the current bias module, the processing module and the mirror image module consists of tunneling field effect transistors. The application also discloses a cell nerve network which comprises the transconductance operation amplification circuit. The transconductance operation amplification circuit and the cell nerve network in the application can help reduce whole circuit integration area and lower power consumption.

Description

technical field [0001] The application relates to the field of integrated circuits and cellular neural networks, in particular to a transconductance operational amplifier circuit and cellular neural networks. Background technique [0002] Cellular Neural Network (CNN) was proposed by Chua and Yang in 1988. Its basic circuit unit is called a cell, which is composed of linear and nonlinear circuit elements. Typical components are linear capacitors, linear resistors, linear and nonlinear controlled power supplies, and independent power supplies. A cellular neural network with a scale of M×N has a total of M×N cells arranged in M ​​rows and N columns. Cellular neural network is a kind of non-linear signal analog processor with local interconnection and double-valued output. These superior properties make cellular neural networks widely used in image processing, pattern recognition, biomedicine, automatic control, and secure communication. [0003] The theoretical design and ...

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Problems solved by technology

[0004] Therefore, the existing cellular neural network circuit structure is complex, the integration area of ​​the overall circuit is large, and the power consumption is relatively high.

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