Harnessing the power of temperature gradient-enhanced pyroelectricity: Self-powered temperature/light detection in Ce-doped HfO2 ferroelectric films with downward spontaneous polarization
Source abstract
Abstract
Ferroelectric materials are ideal for self-powered sensors in Internet of Things (IoT) and high-precision detection systems due to their excellent polarization properties. Compatibility with miniaturization, high-density systems, and complementary metal oxide semiconductor (CMOS) processes is crucial for their widespread adoption. HfO2-based ferroelectric films show potential in self-powered pyroelectric sensors as their thinness enables effective temperature and light detection. However, the disordered ferroelectric domain distribution limits their pyroelectric performance and hampers the development of highly integrated self-powered pyroelectric devices. This report investigates the temperature and light detection capabilities of Ce-doped HfO2 ferroelectric films, which exhibit as-grown spontaneous polarization in the downward direction, making them a promising option for self-powered pyroelectric sensors. The findings provide robust evidence that the introduction of a temperature gradient significantly enhances pyroelectricity. In addition, their applications in the detection of hot/cold wind and breathing have been proved. Notably, the 30 nm thick Ce-doped HfO2 ferroelectric film has a high pyroelectric coefficient of about 894.7 μC·m–2·K–1 and enables high-precision detection of changes in temperature of 0.1 K. This study highlights the potential application of HfO2-based ferroelectric films in self-powered sensors with temperature and light detection capabilities, making them a promising candidate for future IoT-based systems and high-precision detection systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ferroelectric and Negative Capacitance Devices · MXene and MAX Phase Materials · Ferroelectric and Piezoelectric Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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