中文摘要：

A new method of processing positron annihilation lifetime spectra is proposed.It is based on an artificial neural network(ANN)-back propagation network(BPN).By using data from simulated positron lifetime spectra which are generated by a simulation program and tested by other analysis programs,the BPN can be trained to extract lifetime and intensity from a positron annihilation lifetime spectrum as an input.In principle,the method has the potential to unfold an unknown number of lifetimes and their intensities from a measured spectrum.So far,only a proof-of-principle type preliminary investigation was made by unfolding three or four discrete lifetimes.The present study aims to design the network.Besides,the performance of this method requires both the accurate design of the BPN structure and a long training time.In addition,the performance of the method in practical applications is dependent on the quality of the simulation model.However,the chances of satisfying the above criteria appear to be high.When appropriately developed,a trained network could be a very efficient alternative to the existing methods,with a very short identification time.We have used the artificial neural network codes to analyze data such as the positron lifetime spectra for single crystal materials and monocrystalline silicon.Some meaningful results are obtained.

英文摘要：

A new method of processing positron annihilation lifetime spectra is proposed. It is based on an artificial neural network （ANN）-back propagation network （BPN）. By using data from simulated positron lifetime spectra which are generated by a simulation program and tested by other analysis programs, the BPN can be trained to extract lifetime and intensity from a positron annihilation lifetime spectrum as an input. In principle, the method has the potential to unfold an unknown number of lifetimes and their intensities from a measured spectrum. So far, only a proof-of-principle type preliminary investigation was made by unfolding three or four discrete lifetimes. The present study aims to design the network. Besides, the performance of this method requires both the accurate design of the BPN structure and a long training time. In addition, the performance of the method in practical applications is dependent on the quality of the simulation model. However, the chances of satisfying the above criteria appear to be high. When appropriately developed, a trained network could be a very efficient alternative to the existing methods, with a very short identification time. We have used the artificial neural network codes to analyze data such as the positron lifetime spectra for single crystal materials and monocrystalline silicon. Some meaningful results are obtained.