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Ionizing Radiation Effects Spectroscopy for Analysis of Total-Ionizing Dose Degradation in RF Circuits

机译:用于分析射频电路中总电离剂量降解的电离辐射效应光谱

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Ionizing radiation effects spectroscopy (IRES) for the analysis of total-ionizing dose (TID) degradation in radio frequency (RF) circuits is presented. IRES is used to image the change in the operating parameters with respect to TID and for classification of the circuit's state based on image features. Measured data from two voltage-controlled oscillators (VCOs) and two phase-locked loops (PLLs) exposed to TID are used to demonstrate the utility of IRES for radiation effects analysis. The VCO and PLL circuits were designed in a 130-nm bulk CMOS technology. The circuits were irradiated up to 300 krad(SiO2) using an Aracor X-ray source, and IRES images were developed at various TID levels based on the instantaneous frequency of the output waveforms. The IRES technique is based upon RF-distinct native attribute fingerprinting for specific emitter identification using RF waveforms in communication systems. The technique employs statistical time-frequency analyses of various signal features. The resulting statistical measures are utilized to identify the operational state of the circuit (i.e., the TID level and bias voltage) using machine learning (ML) classification. While minor increases in operating frequency were measured with dose, the technique resulted in up to 100% prediction accuracy in the identification of the operational state following an ML training set of at least 50 measured samples. Moreover, the IRES technique enhances standard measurements by capturing global parametric shifts as well as transient variation. The technique, which exploits the subtle characteristics inherent in the waveforms, shows promise in radiation dosimetry applications as well as in-situ monitoring of device and circuit operational health.
机译:提出了用于分析射频(RF)电路中总电离剂量(TID)退化的电离辐射效应谱(IRES)。 IRES用于对工作参数相对于TID的变化进行成像,并基于图像特征对电路状态进行分类。来自暴露于TID的两个压控振荡器(VCO)和两个锁相环(PLL)的测量数据用于证明IRES在辐射效应分析中的实用性。 VCO和PLL电路采用130纳米体CMOS技术设计。使用Aracor X射线源对电路进行高达300 krad(SiO2)的辐照,并根据输出波形的瞬时频率在各种TID电平下生成IRES图像。 IRES技术基于用于通信系统中使用RF波形的特定发射器识别的RF固有属性指纹识别。该技术采用了各种信号特征的统计时频分析。利用机器学习(ML)分类,利用所得的统计量度来识别电路的工作状态(即TID电平和偏置电压)。虽然使用剂量测量了操作频率的微小增加,但在至少训练了50个测量样本的ML训练集之后,该技术在识别操作状态时可达到100%的预测准确性。此外,IRES技术通过捕获全局参数偏移以及瞬态变化来增强标准测量。该技术利用了波形固有的微妙特性,显示出在辐射剂量学应用以及对设备和电路运行状况进行现场监控方面的希望。

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