Silvaco, Inc. is contributing to advancements in midwave-infrared sensing through a detailed study of how material imperfections impact sensor performance. Researchers are combining finite-element analysis (FEA) modeling with both nonequilibrium Green’s function (NEGF) calculations and Gaussian process (GP) regression to comprehensively map internal quantum efficiency in Type-II superlattices, materials increasingly used as alternatives to traditional bulk materials in infrared photodetectors. This multi-method approach is crucial because the curving process used to create these sensors introduces strain within the active region, a factor the research team emphasizes must be considered. The analysis, conducted by John Glennon of Boston University and colleagues, explores both ideal and disordered superlattices to understand the fundamental and practical limits of these materials in curved devices.
Type-II superlattices are rapidly gaining attention as potential replacements for conventional bulk materials in midwave-infrared photodetectors, driven by the demand for more sophisticated infrared sensing capabilities. However, fabricating these sensors introduces significant engineering challenges, particularly when creating curved devices. The curving process, essential for certain sensor designs, inherently induces stress within the active region of the superlattice, directly impacting performance and requiring precise accounting in simulations. FEA plays a crucial role by predicting the specific strain configuration throughout the active region resulting from the device’s curvature; this detailed mapping of stress distribution is then integrated with NEGF calculations, which determine vertical hole mobility under various strain conditions. John Glennon of Boston University explained that FEA is used for predicting the strain configuration throughout the active region induced by the device’s curving procedure, highlighting the method’s importance in understanding material behavior.
The resulting data informs a Gaussian process model, effectively linking predicted quantum efficiency to the spatial coordinates of the curved sensor, allowing for performance prediction under realistic strained conditions. This multi-method approach extends beyond ideal materials, also investigating the impact of superlattice disorder on device performance. By analyzing both perfect and disordered superlattices, the team aims to establish both the theoretical limits and practical constraints of these curved sensors. The research, conducted by a collaboration including scientists from Boston University and HRL Laboratories, LLC, seeks to understand how these materials behave in curved devices, which could lead to optimized designs and improved infrared sensing technology.
The innovation lies in leveraging GP regression to bridge the gap between these computationally intensive methods and spatial variations within the curved device, allowing for a predictive model that maps quantum efficiency onto the device’s coordinates based on the FEA-predicted strain configuration. This analysis extends to both ideal and deliberately disordered superlattices, enabling a thorough understanding of performance limitations stemming from material imperfections as well as fundamental material properties. Crucially, the curving process used to fabricate these sensors introduces significant strain, demanding accurate modeling to predict device behavior; this strain is not merely a fabrication challenge, but a fundamental factor influencing quantum efficiency. The team’s work, detailed in Physics Applied, demonstrates how GP regression can efficiently process data from FEA and NEGF to create a robust performance map, potentially accelerating the development of more sensitive and reliable infrared sensors for a range of applications.
