The Neuron-Bipolar Junction Transistor (vBJT) and Its Applications in Artificial Neural Network Implementation

Autor:	Wen-Cheng Yen, 顏文正
Rok vydání:	2000
Druh dokumentu:	學位論文 ; thesis
Popis:	89 In this thesis, a new device called the neuron-bipolar junction transistor (vBJT) is proposed and analyzed for VLSI implementation of neural networks. The applications of vBJTs on analog Hamming neural network and cellular neural networks (CNNs) are also presented and verified. The whole thesis is divided into three main parts: (1) the analysis and design of the vBJT structure and its application to the implementation of the analog Hamming neural network; (2) the implementation of the new vBJT CNN structure with programmable large-neighborhood symmetrical templates; (3) the implementation of the new vBJT CNN structures with programmable large-neighborhood asymmetrical templates. Firstly, in the proposed vBJT structure, the parasitic PNP bipolar junction transistor in the CMOS process is used to implement the neuron whereas the spreading base resistor array is used to realize the synaptic weights for various neuron inputs. The multi-emitter structure can also be used to realize the multiple neuron outputs. The vBJT neuron cell has the advantages of compact structure and small chip size. The vBJT neuron cell has been successfully applied to the implementation of the analog Hamming neural network. The analog vBJT Hamming network can store many sets of examplar patterns with different gray levels. Moreover, the levels of input patterns can be weighted or scaled to eliminate the common offsets and increase both input dynamic range and processing flexibility. An experimental chip of the proposed vBJT analog Hamming neural network with the cell size of 8x8 has been designed and fabricated by using 0.6um single-poly triple-metal (SPTM) N-well CMOS technology. The analog Hamming neural network has been successfully verified through measurement. With simple and compact structure and high integration capability, the proposed vBJT Hamming network has a great potential in various applications. Secondly, based on the basic device physics of the vBJT, a new compact CNN structure called the vBJT CNN, is proposed and analyzed. In the vBJT CNN, both vBJT and lambda bipolar transistor realized by parasitic pnp BJTs in the CMOS process are used to implement the neuron whereas the coupling MOS resistors are used to realize the symmetrical synapse weights or templates among various neurons. Thus it has the advantages of small chip area and high integration capability. Moreover, the proposed symmetrical vBJT CNN can be easily designed to achieve large neighborhood without extra interconnection. This is the first realized analog large-neighborhood CNN VLSI. By adding a metal-layer optical window to the vBJT, the BJT can be served as the phototransistor and the vBJT CNN can receive optical images as initial state inputs or external inputs. The correct functions of the vBJT CNNs in noise removal, hole filling, and erosion have been successfully verified in HSPICE simulation. An experimental chip containing a 32x32 vBJT CNN and a 16x16 vBJT CNN with phototransistor design, has been designed and fabricated in 0.6um single-poly triple-metal N-well CMOS technology. The fabricated chips have the cell state transition time of 0.8us and the static power consumption of 60 uW/cell. The area density can be as high as 1,270 cells/mm2. The measurement results have also confirmed the correct functions of the proposed vBJT CNNs. Finally, the new vBJT CNN structures with asymmetrical templates and large neighborhood are proposed and analyzed. In the proposed vBJT CNN, the structure of vBJT is incorporated with that of the lambda bipolar transistor (vBJT) and realized by parasitic pnp BJTs in the N-well CMOS technology to implement the neuron. The synaptic weights are realized by a fully programmable stage containing two common-emitter amplifiers formed by two gate-controlled lateral pnp BJTs, two MOS switches, and one coupling MOS resistor. The correct functions of the vBJT CNNs with single neighborhood in edge detection, shadow projection, and connected component detection have been successfully verified in HSPICE simulation. Moreover, the proposed vBJT CNN is further modified to implement large neighborhood without adding too many extra interconnections and circuits. As the demonstrative examples on the applications of the proposed large-neighborhood vBJT CNNs, three function of corner detection, de-blurring, and muller-lyer arrowhead illusion have been successfully realized and verified by HSPICE simulation. An experimental chip of 16x16 vBJT CNN with asymmetrical templates and single neighborhood layer has been designed and fabricated in 0.6um single-poly triple-metal N-well CMOS technology. The fabricated chips have the cell state transition time of 1us and the static power consumption of 264uW/cell. The cell density can be 236 cells/mm2. The measurement results have confirmed the correct functions of the proposed vBJT CNNs. From the above results, it is believed that the proposed BJT structure has a great potential in the implementation of neural network systems for various signal processing applications. Further researches in this field will be conducted in the future.
Databáze:	Networked Digital Library of Theses & Dissertations
Externí odkaz:	http://ndltd.ncl.edu.tw/handle/90508789848484411402 Zobrazit plný text záznamu