Epitaxial GeSn Obtained by High Power Impulse Magnetron Sputtering and the Heterojunction with Embedded GeSn Nanocrystals for Shortwave Infrared Detection

Enhancing SiGeSn nanocrystals SWIR photosensing by high passivation in nanocrystalline HfO2 matrix

SiGeSn nanocrystals (NCs) in oxides are of considerable interest for photo-effect applications due to the fine-tuning of the optical bandgap by quantum confinement in NCs. We present a detailed study regarding the silicon germanium tin (SiGeSn) NCs embedded in a nanocrystalline hafnium oxide (HfO2) matrix fabricated by using magnetron co-sputtering deposition at room temperature and rapid thermal annealing (RTA). The NCs were formed at temperatures in the range of 500-800 °C. RTA was performed to obtain SiGeSn NCs with surfaces passivated by the embedding HfO2 matrix. The formation of NCs and β-Sn segregation were discussed in relation to the deposition and processing conditions by employing HRTEM, XRD and Raman spectroscopy studies. The spectral photosensitivity exhibited up to 2000 nm in short-wavelength infrared (SWIR) depending on the Sn composition was obtained. Comparing to similar results on GeSn NCs in SiO2 matrix, the addition of Si offers a better thermal stability of SiGeSn NCs, while the use of HfO2 matrix results in better passivation of NCs increasing the SWIR photosensitivity at room temperature. These results suggest that SiGeSn NCs embedded in an HfO2 matrix are a promising material for SWIR optoelectronic devices.

Constructing a High-Quality t-Se/Si Interface Via In Situ Light Annealing of a-Se for a Self-Powered Image Sensor

The quality of the interface, e.g., the semiconductor-semiconductor or metal-semiconductor interface, is the main factor restricting the photodetection performance of a heterojunction. In this study, a high-quality Se/Si interface is constructed via in situ directional transformation of amorphous Se (a-Se) into crystalline Se (t-Se) on a Si substrate via light annealing. Benefitting from the high-quality interface and appropriate energy band between Si and Se, the t-Se/Si heterojunction exhibits an extremely high responsivity and detectivity of 583.33 mA W^-1 and 8.52 × 10¹² Jones at 760 nm, respectively. In addition, the device exhibits an ultrafast rise time of 183 µs and a decay time of 405 µs. Furthermore, an image sensor fabricated via local light annealing successfully recognizes patterns of "N," "P," and "U." This study provides valuable guidance for the construction of high-quality interfaces and the design of self-powered image sensors.

GeSn/SiO2 Multilayers by Magnetron Sputtering Deposition for Short-Wave Infrared Photonics

The development of short-wave infrared (SWIR) photonics based on GeSn alloys is of high technological interest for many application fields, such as the Internet of things or pollution monitoring. The manufacture of crystalline GeSn is a major challenge, mainly because of the low miscibility of Ge and Sn. The use of embedded GeSn nanocrystals (NCs) by magnetron sputtering is a cost-effective and efficient method to relax the growth conditions. We report on the use of GeSn/SiO₂ multilayer deposition as a way to control the NC size and their insulation. The in situ prenucleation of NCs during deposition was followed by ex situ rapid thermal annealing. The nanocrystallization of 20×(11nm_Ge_0.865Sn_0.135/1.5nm_SiO₂) multilayers leads to formation of GeSn NCs with ∼16% Sn concentration and ∼9 nm size. Formation of GeSn domes that are vertically correlated contributes to the nanocrystallization process. The absorption limit of ∼0.4 eV in SWIR found by ellipsometry is in agreement with the spectral photosensitivity. The ITO/20×(GeSn NC/SiO₂)/p-Si/Al diodes show a maximum value of the SWIR photosensitivity at a reverse voltage of 0.5 V, with extended sensitivity to wavelengths longer than 2200 nm. The multilayer diodes have higher photocurrent efficiency compared to diodes based on a thick monolayer of GeSn NCs.

GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms

One of the key elements in assessing traffic safety on the roads is the detection of asphalt conditions. In this paper, we propose an optical sensor based on GeSi nanocrystals embedded in SiO₂ matrix that discriminates between different slippery road conditions (wet and icy asphalt and asphalt covered with dirty ice) in respect to dry asphalt. The sensor is fabricated by magnetron sputtering deposition followed by rapid thermal annealing. The photodetector has spectral sensitivity in the 360–1350 nm range and the signal-noise ratio is 10²–10³. The working principle of sensor setup for detection of road conditions is based on the photoresponse (photocurrent) of the sensor under illumination with the light reflected from the asphalt having different reflection coefficients for dry, wet, icy and dirty ice coatings. For this, the asphalt is illuminated sequentially with 980 and 1064 nm laser diodes. A database of these photocurrents is obtained for the different road conditions. We show that the use of both k-nearest neighbor and artificial neural networks classification algorithms enables a more accurate recognition of the class corresponding to a specific road state than in the case of using only one algorithm. This is achieved by comparing the new output sensor data with previously classified data for each algorithm and then by performing an intersection of the algorithms’ results.