Machine learning methods have proven to be powerful in various research fields.In this paper,we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific d...Machine learning methods have proven to be powerful in various research fields.In this paper,we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific discovery approach.The total ionizing dose(TID)effects usually cause gain degradation of bipolar junction transistors(BJTs),leading to functional failures of bipolar integrated circuits.Currently,many experiments of TID effects on BJTs have been conducted at different laboratories worldwide,producing a large amount of experimental data which provides a wealth of information.However,it is difficult to utilize these data effectively.In this study,we proposed a new artificial neural network(ANN)approach to analyze the experimental data of TID effects on BJTs An ANN model was built and trained using data collected from different experiments.The results indicate that the proposed ANN model has advantages in capturing nonlinear correlations and predicting the data.The trained ANN model suggests that the TID hardness of a BJT tends to increase with base current I.A possible cause for this finding was analyzed and confirmed through irradiation experiments.展开更多
Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-...Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-aided design(TCAD) is a powerful simulation tool for electronic devices. This simulation tool has been widely used in the research of radiation effects.However, calibration of TCAD models is time-consuming. In this study, we introduce a fast calibration approach for TCAD model calibration of metal–oxide–semiconductor field-effect transistors(MOSFETs). This approach utilized a machine learning-based surrogate model that was several orders of magnitude faster than the original TCAD simulation. The desired calibration results were obtained within several seconds. In this study, a fundamental model containing 26 parameters is introduced to represent the typical structure of a MOSFET. Classifications were developed to improve the efficiency of the training sample generation. Feature selection techniques were employed to identify important parameters. A surrogate model consisting of a classifier and a regressor was built. A calibration procedure based on the surrogate model was proposed and tested with three calibration goals. Our work demonstrates the feasibility of machine learning-based fast model calibrations for MOSFET. In addition, this study shows that these machine learning techniques learn patterns and correlations from data instead of employing domain expertise. This indicates that machine learning could be an alternative research approach to complement classical physics-based research.展开更多
Objective To investigate the protective effects of purified effective component group in extract from Xiaoshuan Tongluo(CGXT) formula on chronic brain ischemia in rats.Methods CGXT 75,150,and 300 mg/kg or vehicle were...Objective To investigate the protective effects of purified effective component group in extract from Xiaoshuan Tongluo(CGXT) formula on chronic brain ischemia in rats.Methods CGXT 75,150,and 300 mg/kg or vehicle were ig administered daily for four weeks to rats with bilateral common carotid arteries ligation(BCCAL) .From the day 24 to 28 after BCCAL,Morris water maze was performed to assess the learning and memory impairment of rats.Four weeks after BCCAL,brain gray and white matter damage were assessed.Results In Morris test,the mean escape latency of rats in the CGXT(150 and 300 mg/kg) groups was significantly shorter than that in the vehicle group.CGXT also attenuated the neuronal damage in hippocampus and cortex and reduced the pathological damage in the optic tract and corpus callosum.Conclusion CGXT could improve learning and memory impairment resulted from BCCAL in rats.These results provide the experimental basis for the clinical use of CGXT in stroke treatment and may help in investigation of multimodal therapy strategies in ischemic cerebrovascular diseases including stroke.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 11690040 and 11690043)。
文摘Machine learning methods have proven to be powerful in various research fields.In this paper,we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific discovery approach.The total ionizing dose(TID)effects usually cause gain degradation of bipolar junction transistors(BJTs),leading to functional failures of bipolar integrated circuits.Currently,many experiments of TID effects on BJTs have been conducted at different laboratories worldwide,producing a large amount of experimental data which provides a wealth of information.However,it is difficult to utilize these data effectively.In this study,we proposed a new artificial neural network(ANN)approach to analyze the experimental data of TID effects on BJTs An ANN model was built and trained using data collected from different experiments.The results indicate that the proposed ANN model has advantages in capturing nonlinear correlations and predicting the data.The trained ANN model suggests that the TID hardness of a BJT tends to increase with base current I.A possible cause for this finding was analyzed and confirmed through irradiation experiments.
基金supported by the National Natural Science Foundation of China (Nos. 11690040 and 11690043)。
文摘Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-aided design(TCAD) is a powerful simulation tool for electronic devices. This simulation tool has been widely used in the research of radiation effects.However, calibration of TCAD models is time-consuming. In this study, we introduce a fast calibration approach for TCAD model calibration of metal–oxide–semiconductor field-effect transistors(MOSFETs). This approach utilized a machine learning-based surrogate model that was several orders of magnitude faster than the original TCAD simulation. The desired calibration results were obtained within several seconds. In this study, a fundamental model containing 26 parameters is introduced to represent the typical structure of a MOSFET. Classifications were developed to improve the efficiency of the training sample generation. Feature selection techniques were employed to identify important parameters. A surrogate model consisting of a classifier and a regressor was built. A calibration procedure based on the surrogate model was proposed and tested with three calibration goals. Our work demonstrates the feasibility of machine learning-based fast model calibrations for MOSFET. In addition, this study shows that these machine learning techniques learn patterns and correlations from data instead of employing domain expertise. This indicates that machine learning could be an alternative research approach to complement classical physics-based research.
基金National Natural Science Foundation of China (30630073)
文摘Objective To investigate the protective effects of purified effective component group in extract from Xiaoshuan Tongluo(CGXT) formula on chronic brain ischemia in rats.Methods CGXT 75,150,and 300 mg/kg or vehicle were ig administered daily for four weeks to rats with bilateral common carotid arteries ligation(BCCAL) .From the day 24 to 28 after BCCAL,Morris water maze was performed to assess the learning and memory impairment of rats.Four weeks after BCCAL,brain gray and white matter damage were assessed.Results In Morris test,the mean escape latency of rats in the CGXT(150 and 300 mg/kg) groups was significantly shorter than that in the vehicle group.CGXT also attenuated the neuronal damage in hippocampus and cortex and reduced the pathological damage in the optic tract and corpus callosum.Conclusion CGXT could improve learning and memory impairment resulted from BCCAL in rats.These results provide the experimental basis for the clinical use of CGXT in stroke treatment and may help in investigation of multimodal therapy strategies in ischemic cerebrovascular diseases including stroke.
