This paper presents an uncooled monolithic resonant triple-band terahertz (THz) thermal detector using a 55 nm complementary metal oxide semiconductor (CMOS) process. The novel integration of a compact triple-band metamaterial (MM) absorber coupled with a sensitive proportional to absolute temperature (PTAT) sensor leads to a compact, uncooled, cost-effective, easy-integration, and massproduction CMOS integrated triple-band THz thermal detector. The principles of operation and physical modeling such as selective absorption of electromagnetic energy, thermal energy conversion, and electrical response are demonstrated. The triple-band MM absorber achieves high absorption magnitudes of 90.4%, 96.8%, and 98.8% at 0.91, 2.58, and 4.3 THz respectively. The optimized PTAT sensor shows a high temperature coefficient of voltage of 10.11 mV/°C. Simulated responsivities are 37.28 V/W at 0.91 THz, 49.9 V/W at 2.58 THz, and 61.11 V/W at 4.3 THz. The proposed triple-band THz thermal detector shows calculated noise equivalence powers (NEPs) of 1.07, 1.6, and 1.31 μW/Hz0.5 at 0.91, 2.58, and 4.3 THz respectively. Relatively better experimental results are obtained at 0.91 THz and 2.58 THz due to the scarcity of THz sources. The proposed detector achieves maximum responsivities of 33.4 V/W and 47.9 V/W, while minimum NEPs of 1.49 μW/Hz0.5 at 0.91 THz with a modulation frequency of 2 Hz, and 1.88 μW/Hz0.5 at 2.58 THz with a modulation frequency of 0.5 Hz.

Multiband terahertz (THz) detectors have attractive prospects in the areas of THz sensing and imaging. This paper presents a monolithic resonant CMOS fully integrated triple-band THz thermal detector that is composed of a compact loop antenna and an optimized proportional to absolute temperature (PTAT) sensor, leading to an uncooled, compact, low-cost, easy-integration, and mass-production multiband detector. The principles of operation, theoretical calculation, and experimental validation are demonstrated in detail. Calculated responsivities are 34.9 V/W, 51.6 V/W, and 47.6 V/W at the operation frequencies of 0.91 THz, 2.58 THz, and 4.3 THz, respectively, for the natural atmospheric windows. Relatively better experimental results are obtained at 0.91 THz and 2.58 THz due to the scarcity of THz sources, showing maximum responsivities of 29.2 V/W and 46.5 V/W with minimum NEPs of 1.57 µW/Hz^0.5 and 1.26 µW/Hz^0.5, respectively. The presented triple-band thermal detector has the natural scalability to focal plane arrays, providing great potential for multiband THz sensing and imaging systems.

Optical Engineering (OE) publishes peer-reviewed papers reporting on research, development, and applications of optics, photonics, and imaging science and engineering.

In this study, a low-cost space mapping (SM) modeling method with mesh deformation is proposed for microwave components. In this approach, the coarse-mesh model with mesh deformation is developed as the coarse model, and the fine-mesh model is simulated as the fine model. The SM technique establishes the mapping relationship between the coarse-mesh model and the fine-mesh model. This approach enables us to combine the computational efficiency of the coarse model with the accuracy of the fine model. The automatic mesh deformation technology is embedded in the coarse model to avoid the discontinuous change in the electromagnetic response. The proposed model consisting of the coarse model and two mapping modules can represent the features of the fine model more accurately, and predict the electromagnetic response of microwave components quickly. The proposed mesh SM modeling technique is applied to the four-pole waveguide filter. The value for the training and test errors in the proposed model is less than 1%, which is lower than that for the ANN models and the existing SM models trained with the same data. Compared with HFSS software, the proposed model can save about 70% CPU time in predicting a set of 100 data. The results show that the proposed method achieves a good modeling accuracy and efficiency with few training data and a low computational cost.