Proton induced radiation effect of SiC MOSFET under different bias

Radiation effects of silicon carbide metal–oxide–semiconductor field-effect transistors(SiC MOSFETs)induced by 20 MeV proton under drain bias(V_(D)=800 V,V_(G)=0 V),gate bias(V_(D)=0 V,V_(G)=10 V),turn-on bias(V_(D)=0.5 V,V_(G)=4 V)and static bias(V_(D)=0 V,V_(G)=0 V)are investigated.The drain current of SiC MOSFET under turn-on bias increases linearly with the increase of proton fluence during the proton irradiation.When the cumulative proton fluence reaches 2×10^(11)p·cm^(-2),the threshold voltage of SiC MOSFETs with four bias conditions shifts to the left,and the degradation of electrical characteristics of SiC MOSFETs with gate bias is the most serious.In the deep level transient spectrum test,it is found that the defect energy level of SiC MOSFET is mainly the ON2(E_(c)-1.1 eV)defect center,and the defect concentration and defect capture cross section of SiC MOSFET with proton radiation under gate bias increase most.By comparing the degradation of SiC MOSFET under proton cumulative irradiation,equivalent 1 MeV neutron irradiation and gamma irradiation,and combining with the defect change of SiC MOSFET under gamma irradiation and the non-ionizing energy loss induced by equivalent 1 MeV neutron in SiC MOSFET,the degradation of SiC MOSFET induced by proton is mainly caused by ionizing radiation damage.The results of TCAD analysis show that the ionizing radiation damage of SiC MOSFET is affected by the intensity and direction of the electric field in the oxide layer and epitaxial layer.

Effect of moxibustion on mTOR-mediated autophagy in rotenone-induced Parkinson's disease model rats

Defects in autophagy-mediated clearance of α-synuclein may be one of the key factors leading to progressive loss of dopaminergic neurons in the substantia nigra. Moxibustion therapy for Parkinson’s disease has been shown to have a positive effect, but the underlying mechanism remains unknown. Based on this, we explored whether moxibustion could protect dopaminergic neurons by promoting autophagy mediated by mammalian target of rapamycin （mTOR）, with subsequent elimination of α-syn. A Parkinson’s disease model was induced in rats by subcutaneous injection of rotenone at the back of their necks, and they received moxibustion at Zusanli （ST36）, Guanyuan （CV4）and Fengfu （GV16）, for 10 minutes at every point, once per day, for 14 consecutive days. Model rats without any treatment were used as a sham control. Compared with the Parkinson’s disease group, the moxibustion group showed significantly greater tyrosine hydroxylase immunoreactivity and expression of light chain 3-II protein in the substantia nigra, and their behavioral score, α-synuclein immunoreactivity,the expression of phosphorylated mTOR and phosphorylated ribosomal protein S6 kinase （p-p70S6K） in the substantia nigra were significantly lower. These results suggest that moxibustion can promote the autophagic clearance of α-syn and improve behavioral performance in Parkinson’s disease model rats. The protective mechanism may be associated with suppression of the mTOR/p70S6K pathway.

Application of the asynchronous advantage actor–critic machine learning algorithm to real-time accelerator tuning

This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation.Therefore,parameter optimization methods such as pointwise scanning,evolutionary algorithms(EAs),and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency.However,it is difficult for them to deal with a large number of discrete local optima.The A3C algorithm,which has been applied in the automated control field,provides an approach for improving multi-dimensional optimization.The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators.Experiments in which optimization is achieved by using pointwise scanning,the genetic algorithm(one kind of EAs),and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section(LEBT)of the Xi’an Proton Application Facility.Optimal currents are determined when the highest transmission of a radio frequency quadrupole(RFQ)accelerator downstream of the LEBT is achieved.The optimal work points of the tuned accelerator were obtained with currents of 0 A,0 A,0 A,and 0.1 A,for the four steering magnets,and 107 A and 96 A for the two solenoids.Furthermore,the highest transmission of the RFQ was 91.2%.Meanwhile,the lower time required for the optimization with the A3C algorithm was successfully verified.Optimization with the A3C algorithm consumed 42%and 78%less time than pointwise scanning with random initialization and pre-trained initialization of weights,respectively.

Beam position monitors as precise phase pickups for beam energy measurement at the Compact Pulsed Hadron Source

The 13-MeV proton linac of the Compact Pulsed Hadron Source(CPHS) at Tsinghua University, China,is composed of a 50-keV electron cyclotron resonance proton source, a 3-MeV four-vane-type radio-frequency quadrupole(RFQ) accelerator, and a drift tube linac(DTL). Precise measurement of the beam energies at the exit of the RFQ and the DTL is critical for DTL commissioning. Two button-type beam position monitors(BPMs) installed downstream of the RFQ are used to perform the measurement using a time-of-flight method. The effects of several factors on phase measurement accuracy are analyzed. The phase measurement accuracy of the BPMs at CPHS is better than ± 1.03° at 325 MHz after corrections,corresponding to an energy measurement error of± 0.07%. The beam energy measured at the exit of the RFQ is 2.994 ± 0.0022 MeV,which is consistent with the design value.