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Charge coupled device (CCD)-based thermoreflectance imaging using a “4-bucket” lock-in imaging algorithm is a well-established, powerful method for obtaining high spatial and thermal resolution two-dimensional thermal maps of optoelectronic, electronic, and micro-electro-mechanical systems devices. However, the technique is relatively slow, limiting broad commercial adoption. In this work, we examine the underlying limit on the image acquisition speed using the conventional “4-bucket” algorithm and show that the straightforward extension to an n-bucket technique by faster sampling does not address the underlying statistical bias in the data analysis and hence does not reduce the image acquisition time. Instead, we develop a modified “enhanced n-bucket” algorithm that halves the image acquisition time for every doubling of the number of buckets. We derive detailed statistical models of the algorithms and confirm both the models and the resulting speed enhancement experimentally, resulting in a practical means of significantly enhancing the speed and utility of CCD-based frequency domain, homodyne thermoreflectance imaging.

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