仿生資料庫

Database of Photoresponsed Zinc-Tin Oxide Transistor with Characteristics Electrical Property (光響應之氧化鋅錫電晶體特徵電性資料庫)

Introduction

Due to the characteristic electrical properties (including current extreme value, transistor startup bias, threshold voltage, carrier mobility, and subcritical swing) produced by zinc tin oxide transistors for special incident light sources, thickness of zinc tin oxide transistors, oxide is patterned by lithography and etching for defining the zinc tin oxide film area, the channel length between the source and drain electrodes, and light sources of different intensities. Therefore, this database establishes a high-accuracy database based on the above-mentioned characteristic electrical properties and the composition conditions and operating conditions of the zinc tin oxide transistors through the combination of genetic algorithm and neural network. The learning process is up to 1,000,000 times, the root mean square (RMS) error is less than 0.0014, and the false rate is less than 0.01.

由於氧化鋅錫電晶體對特殊入射光源所產生的特徵電性(包含電流極值、電晶體啟動偏壓、臨界電壓、載子遷移率、次臨界擺幅)會根據氧化鋅錫電晶體之薄膜厚度、有無進行微影蝕刻定義鋅錫氧化物薄膜面積、源極電極和汲極電極間的通道長度以及不同強度的光源等因素而產生變化。因此本資料庫跟據上述之特徵電性與光響應氧化鋅錫電晶體之組成條件以及操作條件透過基因演算法結合類神經網路建立高準確性之資料庫。學習程序為1,000,000次，均方根（RMS）誤差小於0.0014，誤率則小於0.01。