{"id":23811,"date":"2025-04-18T15:31:29","date_gmt":"2025-04-18T15:31:29","guid":{"rendered":"https:\/\/liquidinstruments.com\/?p=23811"},"modified":"2025-06-11T23:54:10","modified_gmt":"2025-06-11T23:54:10","slug":"measuring-single-photon-events-with-reconfigurable-fpga-based-instrumentation-qa-recap","status":"publish","type":"post","link":"https:\/\/liquidinstruments.com\/blog\/measuring-single-photon-events-with-reconfigurable-fpga-based-instrumentation-qa-recap\/","title":{"rendered":"Measuring single-photon events with reconfigurable, FPGA-based instrumentation: Q&A recap","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

This recap and Q+A complement our webinar, <\/span>Measuring single-photon events with reconfigurable, FPGA-based instrumentation,<\/b> which we co-hosted with Laser Focus World on April 15, 2025. If you weren\u2019t able to attend live, you can register now for on-demand access here.<\/a><\/span><\/p>\n

In addition to a webinar summary, we\u2019re providing in-depth answers to select audience questions below.<\/span><\/p>\n

Webinar recap<\/span><\/h1>\n

During this presentation, we gave an overview of <\/span>photon counting<\/span><\/a>, both in terms of physical setup and potential applications, including advantages, challenges, and critical requirements. We introduced the FPGA-based Moku platform and its reconfigurable suite of test and measurement instruments, focusing on the Moku <\/span>Time & Frequency Analyzer<\/span><\/a>, which can act as an efficient photon counting solution due to its combination of high data throughput, parallel processing, and zero dead time.\u00a0\u00a0<\/span><\/p>\n

In a live demonstration, we deployed the Time & Frequency Analyzer alongside other instruments using <\/span>Multi-Instrument Mode<\/span><\/a>. First, we generated a sequence of digital data and encoded it onto a pulsed carrier wave through pulse-position modulation (PPM), after which we detected and decoded the waveform with the Time & Frequency Analyzer. For the second demo, we simulated a Hanbury-Brown-Twiss setup using electrical pulses and an Arduino Uno. We observed the histogram populate in real time and showed how to collect export timestamped data using the Time & Frequency Analyzer.<\/span><\/p>\n

Questions from the audience<\/span><\/h1>\n

Why, exactly, is measuring single-photon events more accurate, especially in applications such as long-distance LiDAR?<\/strong><\/h2>\n

In LiDAR, information is encoded in either the modulation of amplitude, phase, or frequency (for a continuous source), or the spacing of pulses (for a pulsed source). When traveling over short distances, the fidelity of the returning signal is generally high. However, when sending information over long distances, the signal is subject to heavy interference from the atmosphere, attenuating the return power to low levels.\u00a0<\/span><\/p>\n

When using an analog detector in this case, it can be overwhelmed by background noise, from both thermal processes and any amplifiers present. This approach results in a poor SNR and can make coherent demodulation difficult. By contrast, single-photon detectors only \u201cclick\u201d when they detect a real photon event, eliminating the input noise introduced by the analog-to-digital converter. Detecting single photons also allows for accurate timestamping, the ability to \u201cgate\u201d signals \u2014 listening for a signal only when you expect it, and the ability average over many iterations. These factors can increase the SNR compared to coherent demodulation methods.\u00a0<\/span><\/p>\n

In quantum optics, there is the g2 parameter that measures auto-correlation effects (coincident rate). Can you comment about how to quantify the g2 parameter?<\/strong><\/h2>\n

The second-order correlation function, g<\/span>2<\/span>(\ud835\udf0f), measures the probability of detecting two photons separated by a time delay \ud835\udf0f, which is normalized to the value expected from uncorrelated photons. The value at \ud835\udf0f=0 is of particular importance, as this can infer information about the light source. For example:<\/span><\/p>\n