As+注入硅的紫外-可见椭圆偏振光谱

本文采用椭圆偏振光谱法研究了剂量为1×10¹⁶—3×10¹²cm^-2的As⁺注入硅,及其在700℃退火后的光学性质。得出:当As⁺注入剂量增大到某一程度后,便呈非晶特性。低于临界剂量的样品,其n-λ,ε₂-λ关系曲线随剂量的增大而往下方移动,呈有规律变化;退火后,在大于4000?波段,n-λ与ε₂-λ曲线基本恢复到单晶硅状态。但在小于4000?的紫外区却未完全恢复,注入剂量越大,偏离单晶硅就越大。并指出,紫外光区是离子注入硅的信息敏感区;用有效质量模型计算出注入剂量与损伤度的关系。计算结果与实验符合得较好。 关键词：     

Determination of quantum toric error correction code threshold using convolutional neural network decoders

Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.     

；As+注入硅的紫外-可见椭圆偏振光谱

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