AI 中文总结
该研究开发了一款集成ADC、DAC与多通道脉冲发生器的FPGA单元，结合定制固件与Atomize软件，实现了脉冲EPR波谱的近100%时间效率，支持非均匀采样，适用于多种脉冲序列协议。
AI 中文摘要
本文介绍了一套用于脉冲电子顺磁共振（EPR）波谱的硬件与软件系统，其基于单张PCI Express（PCIe）卡构建，板载集成了16位、有效采样率为10 GS/s的数模转换器（DAC）、2.5 GS/s、14位的模数转换器（ADC）以及11通道脉冲发生器。定制固件可在实验运行过程中并行重新加载脉冲发生器与DAC缓冲区，并传输数字化数据，确保无任何数据输入或输出的时间损耗。该单元被集成到开源Atomize软件中，通过高级Python应用程序编程接口（API）在脉冲序列层面进行控制。采用四脉冲双电子-电子共振（DEER）序列进行的基准测试显示，该卡针对每次脉冲序列积累时间仅为几毫秒的情况，即使对于要求严苛的任意波形发生器（AWG）协议，也能达到接近100%的时间效率。尽管该效率是针对单一序列进行演示的，但已证明其对脉冲数量、测量点数量或相位循环深度均不敏感，因此对于任何脉冲重复时间超过该卡约2毫秒重新编程时间的协议，预计均可获得相当的性能。这种动态重新编程方式可使每个后续序列完全独立，还支持非均匀采样与采集。本文阐述了该系统设计、其固件、Python API及其在脉冲EPR应用中的性能。
英文摘要
A hardware and software system for pulsed electron paramagnetic resonance (EPR) spectroscopy is presented, built around a single PCI Express (PCIe) card that combines a 16-bit digital-to-analog converter (DAC) with a 10 GS/s effective sample rate, a 2.5 GS/s 14-bit analog-to-digital converter (ADC), and an 11-channel pulse generator on board. Custom firmware reloads the pulse-generator and DAC buffers and streams the digitized data in parallel with a running experiment, so that no time is lost on data input or output. The unit is integrated into the open-source Atomize software and controlled at the level of pulse sequences through a high-level Python application programming interface (API). Benchmarking with a four-pulse double electron-electron resonance (DEER) sequence shows that the card reaches close to 100% time efficiency for accumulation times of a few milliseconds per pulse sequence, even for demanding arbitrary waveform generator (AWG) protocols. Although demonstrated for a single sequence, the efficiency proved insensitive to the number of pulses, the number of measurement points, or the phase-cycling depth, so that comparable performance is expected for any protocol whose shot repetition time exceeds the ~2 ms reprogramming time of the card. The on-the-fly reprogramming, which makes every successive sequence fully independent, also enables nonuniform sampling and acquisition. The system design, its firmware, the Python API, and its performance in pulsed EPR applications are described.
Comments12 pages with 6 figures
