High‐Performance Single‐Crystal Diamond Detector for Accurate Pulse Shape Discrimination Based on Self‐Organizing Map Neural Networks

Abstract

Herein, a high‐performance single‐crystal diamond (SCD) detector (4.5 × 4.5 ×0.3 mm3) to achieve accurate pulse shape discrimination, which is critical for source tracking in harsh and complex radiation conditions, is demonstrated. Enabled by a deep learning algorithm based on self‐organizing map (SOM) neural networks, and using the transient current technique (TCT) for sampling the detector's response to γ, α, and neutron radiation fields, the SCD detector achieves high recognition accuracy of 97.51%. The SCD detector exhibits a low leakage current of 0.75 pA mm−2 under an electric field of 0.51 V μm−1, and its response to 238Pu α‐rays shows that the charge collection efficiency for electrons and holes is as high as 99.2 and 98.8% respectively, with an energy resolution as low as 1.42%. The results indicate that the high‐performance SCD detector assisted by the machine learning algorithm can effectively distinguish α‐particles and γ‐rays with a potential application in separating the neutron and γ events as well.

Keywords

• detectors
• diamonds
• machine learning
• neural network algorithms
• pulse shape discrimination
• ultrawide-bandgap semiconductors