X射线光谱中特征峰漂移校正算法的研究

Study on Correction Algorithms of Characteristic Peak Drift in X-Ray Spectrum

针对采用数字慢三角成形算法的高性能硅漂移探测器在开关复位型前放中出现的突变脉冲以及该类脉冲在成形后因幅度受损造成的特征峰漂移问题,提出了一种基于突变脉冲修复的特征峰漂移校正算法,该算法包括以下几个流程,首先将该电路输出的弱电流信号经CR微分电路进行转换得到负指数信号,然后负指数信号经三级放大电路放大后的幅度范围为0~2 V,该幅度范围保持在后端模数转换器的处理范围中,对放大后的负指数信号进行模数转换得到数字化的负指数脉冲序列,通过对上述负指数脉冲序列的采样点进行判断,当出现连续多个为零的采样点时就标记该脉冲为突变脉冲,最后对突变脉冲分别调用快校正和慢校正算法进行修复,并将修复后的负指数脉冲序列分别进行数字梯形成形,其成形结果存储到FIFO中进行多道成谱。实验以自制的铁矿样品为测量对象,将未进行校正的原始谱与采用不同校正方法得到的谱图进行对比,校正后铁和锶特征峰的影子峰所在道址区间的计数相比于未校正的原始谱的计数率有了明显的降低,与此同时,铁和锶两个特征峰所在道址区间的计数相比于不校正则有了明显的提高。由于特征峰计数率的漂移正是产生影子峰的根本原因,因此同一种元素在影子峰区域计数率的减小值与在特征峰区域计数率的增加值在数值上应趋于一致,实验结果中铁元素的影子峰和特征峰所在区间快校正和慢校正前后的计数率差值基本符合这一趋势,但锶元素影子峰和特征峰所在区间的快校正前后计数率差值相差较大,不符合影子峰计数减小值即为特征峰计数增加值的规律。造成这种结果的根本原因在于快校正对突变脉冲的修复不完整,而慢校正可以较好地实现所有采样点的修复,最后得出的修复效率也表明对于同样的区间,慢校正法得到的修复效率更高,对特征峰漂移的校正效果更好。结果表明特征峰漂移校正算法可以有效地消除特征峰前面的影子峰,实现对特征峰漂移的校正,这对获取精细X射线谱具有重要意义。

In order to solve the problem of abrupt pulse appearing in switch reset preamplifier of high-performance silicon drift detector using digital slow triangulation algorithm and the problem of characteristic peak drift caused by amplitude damage of such pulse after forming,a correction algorithm of characteristic peak drift based on abrupt pulse repair is proposed.The algorithm includes the following processes.Firstly,the weak current signal output by the former circuit is converted into the negative exponential signal by CR differential circuit,and then,the amplitude range of the negative index signal amplified by the three-stage amplifier is 0~2 V,and the amplitude range is kept in the processing range of the back-end analog-to-digital converter.The digital negative index pulse sequence is obtained by the analog-to-digital conversion of the amplified negative index signal.Through the judgment of the sampling points of the above negative index pulse sequence,when the continuous multiple sampling points are zero,the pulse is marked as abrupt pulses.At last,the fast and slow correction algorithms are used to repair the abrupt pulses,and the repaired negative index pulse sequence is processed by digital trapezoid forming,and the forming results are stored in FIFO for multi-channel spectrum generation.In the experiment,self-made iron ore samples were taken as the measurement object,and the uncorrected original spectrum is compared with the spectrum obtained by different correction methods.The results show that the counting rate of the shadow peaks of the corrected Fe and Sr characteristic peaks in the channel address range is significantly lower than that of the uncorrected original spectrum.At the same time,the number of the channel address intervals of the two characteristic peaks of Fe and Sr is significantly higher than that of uncorrected.Because the drift of the counting rate of the characteristic peak is the root cause of the shadow peak,the decreasing value of the counting rate of the same element in the shadow peak area should be consistent with the increasing value of the counting rate in the characteristic peak area.The results show that the difference of counting rate between the fast correction and slow correction of the shadow peak and the characteristic peak of Fe element is basically consistent with this trend,but the difference of counting rate between the fast correction and the fast correction of strontium element shadow peak and the characteristic peak of Sr element is large,which does not conform to the rule that the decrease of the shadow peak count is the increase of the characteristic peak count.The basic reason for this result is that the fast correction is not complete for the repair of abrupt pulse,and the slow correction can better achieve the repair of all sampling points.The final repair efficiency also shows that for the same interval,the slow correction method has higher repair efficiency and better correction effect for the characteristic peak drift.The results show that the correction algorithm can effectively eliminate the shadow peak in front of the characteristic peak and realize the correction of the characteristic peak drift,which is of great significance for obtaining the fine X-ray spectrum.