To design semiconductor-based optical devices, the optical properties of the used semiconductor materials must be precisely measured over a large spectral band. Transmission spectroscopy stands out as an inexpensive and widely available method for this measurement but requires model assumptions and reconstruction algorithms to convert the measured transmittance spectra into optical properties of the thin films. Amongst the different reconstruction techniques, inverse synthesis methods generally provide high precision but rely on rigid analytical models of a thin film system. In this paper, we demonstrate a flexible inverse synthesis method that uses angular spectrum wave propagation and does not rely on rigid model assumptions. Amongst other evaluated parameters, our algorithm is capable of evaluating the geometrical properties of thin film surfaces, which reduces the variance caused by inverse synthesis optimization routines and significantly improves measurement precision. The proposed method could allow for the characterization of "uncommon" thin film samples that do not fit the current model assumptions, as well as the characterization of samples with higher complexity, e.g., multi-layer systems.

We propose a novel optimization approach for determining the envelopes of thin-film transmittance spectra. This method can be coupled with the Swanepoel algorithm to determine the optical properties of the thin films.

Historically, spectroscopic techniques have been essential for studying the optical properties of thin solid films. However, existing formulae for both normal transmission and reflection spectroscopy often rely on simplified theoretical assumptions, which may not accurately align with real-world conditions. For instance, it is common to assume (1) that the thin solid layers are deposited on completely transparent thick substrates and (2) that the film surface forms a specular plane with a relatively small wedge angle. While recent studies have addressed these assumptions separately, this work presents an integrated framework that eliminates both assumptions simultaneously. In addition, the current work presents a deep review of various formulae from the literature, each with their corresponding levels of complexity. Our review analysis highlights a critical trade-off between computational complexity and expression accuracy, where the newly developed formulae offer enhanced accuracy at the expense of increased computational time. Our user-friendly code, which includes several classical transmittance and reflectance formulae from the literature and our newly proposed expressions, is publicly available in both Python and Matlab at this link.

We have developed an efficient simulator for polarized light microscopy experiments. It supports calculations for multiple analyzer angles across different channels of a polarized camera, enhancing imaging capabilities. Our model is publicly available here.

We introduce a novel Monte Carlo approach for simulating charge propagation in semiconductor detectors, accounting for Coulomb repulsion and diffusion over time.

Semiconductor detectors for high-energy sensing (X/γ-rays) play a critical role in fields such as astronomy, particle physics, spectroscopy, medical imaging, and homeland security. The increasing need for precise detector characterization highlights the importance of developing advanced digital twins, which help optimize the design and performance of imaging systems. Current simulation frameworks primarily focus on modeling electron–hole pair dynamics within the semiconductor bulk after the photon absorption, leading to the current signals at the nearby electrodes. However, most simulations neglect charge diffusion and Coulomb repulsion, which spatially expand the charge cloud during propagation due to the high complexity they add to the physical models. Although these effects are relatively weak, their inclusion is essential for achieving a high-fidelity replication of real detector behavior. There are some existing methods that successfully incorporate these two phenomena with minimal computational cost, including those developed by Gatti in 1987 and by Benoit and Hamel in 2009. The present work evaluates these two approaches and proposes a novel Monte Carlo technique that offers higher accuracy in exchange for increased computational time. Our new method enables more realistic performance predictions while remaining within practical computational limits.

Room temperature semiconductor radiation detectors (RTSD) for X-ray and gamma-ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades with desired detection properties. In a typical pixelated configuration, CZT have electrodes on opposite ends. For advanced event reconstruction algorithms at sub-pixel level, detailed characterization of the RTSD is required in three dimensional (3D) space. However, 3D characterization of the material defects and charge transport properties in the sub-pixel regime is a labor-intensive process with skilled manpower and novel experimental setups. Presently, state-of-art characterization is done over the bulk of the RTSD considering homogenous properties. In this paper, we propose a novel physics based machine learning (PBML) model to characterize the RTSD over a discretized sub-pixelated 3D volume which is assumed. Our novel approach is the first to characterize a full 3D charge transport model of the RTSD. In this work, we first discretize the RTSD between a pixelated electrodes spatially in 3D - x, y, and z. The resulting discretizations are termed as voxels in 3D space. In each voxel, the different physics based charge transport properties such as drift, trapping, detrapping and recombination of charges are modeled as trainable model weights. The drift of the charges considers second order non-linear motion which is observed in practice with the RTSDs. Based on the electron-hole pair injections as input to the PBML model, and signals at the electrodes, free and trapped charges (electrons and holes) as outputs of the model, the PBML model determines the trainable weights by backpropagating the loss function. The trained weights of the model represents one-to-one relation to that of the actual physical charge transport properties in a voxelized detector.

We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. The camera concept is based on Synthetic Wavelength Interferometry, a technique that allows retrieval of depth maps of objects with optically rough surfaces at submillimeter depth precision. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their native chip resolution (as of today, theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model of the object in single-shot, meaning that no temporal sequence of exposures or temporal illumination modulation (such as amplitude or frequency modulation) is necessary, which makes our camera robust against object motion. In this paper, we introduce the novel camera concept and show the first measurements that demonstrate the capabilities of our system. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution (the native pixel resolution of our used detector) and up to sub-mm depth precision. We also report a "single-shot 3D video" acquisition and a first single-shot "Non-Line-of-Sight" measurement. Our technique has great potential for high-precision applications with dynamic object movement, e.g., in AR/VR, industrial inspection, medical imaging, and imaging through scattering media like fog or human tissue.

We present a novel Deep Learning technique based on a CNN-LSTM architecture that directly performs the optical characterization of thin-film materials from their UV-VIS-IR transmission spectra.

Photon-counting detectors based on CZT are essential in nuclear medical imaging, particularly for SPECT applications. Although CZT detectors are known for their precise energy resolution, defects within the CZT crystals significantly impact their performance. These defects result in inhomogeneous material properties throughout the bulk of the detector. The present work introduces an efficient computational model that simulates the operation of semiconductor detectors, accounting for the spatial variability of the crystal properties. Our simulator reproduces the charge-induced pulse signals generated after the X/gamma-rays interact with the detector. The performance evaluation of the model shows an RMSE in the signal below 0.70 function as a digital twin to accurately replicate the operation of actual detectors. Thus, it can be used to mitigate and compensate for adverse effects arising from crystal impurities.

Light-in-flight (LiF) measurements enable the visualization of light paths through arbitrary, volumetric scenes, making light-matter interactions at ultrafast timescales visible. Traditionally, LiF measurements require specialized equipment, such as ultrashort pulse light sources and high-speed electronics, often limited by low spatial resolution. Herein, we introduce a novel computational approach,"Synthetic Light-in-Flight" (SLiF), that overcomes these constraints by relying solely on tunable, continuous wave (CW) lasers and off-the-shelf CMOS cameras. From multiple CW scene measurements at different optical wavelengths, we create multiple "synthetic fields," each at a "synthetic wavelength," which is the beat wave of two respective optical waves. These synthetic fields are robust to speckle and environmental fluctuations, enabling us to combine multiple synthetic fields into a "synthetic light pulse" that sections the volumetric scene. Additionally, we demonstrate that these complex synthetic pulse fields can be freely manipulated in the computer after their acquisition, allowing for spatial and temporal shaping of different sets of pulses from the same set of measurements to maximize the decoded information output for each scene. Finally, we show that the recovered time-of-flight information can be used to characterize physical scene properties, such as depth and refractive indices.

While thin film transmission spectroscopy systems can measure semiconductor optical properties, the utilized optimization-based evaluation methods often introduce variances in the results. We introduce a method of optimizing film surface shapes that reduces this uncertainty.

Imaging through scattering media still presents a challenge for modern optical imaging techniques. The problem becomes even worse for dynamic scattering scenes, e.g., to capture dynamic objects behind a scatterer or then the scattering medium itself is in constant movement, such as fog or living tissue. This contribution introduces a first step towards a potential solution, which builds upon our previous Synthetic Wavelength Imaging techniques. We showcase the feasibility of our approach by imaging an extended hidden object through a scatterer in single-shot.

Copper-nitride (Cu3N) semiconductor material is attracting much attention as a potential, next-generation thin-film solar light absorber in solar cells. In this communication, polycrystalline covalent Cu3N thin films were prepared using reactive-RF-magnetron-sputtering deposition, at room temperature, onto glass and silicon substrates. The very-broadband optical properties of the Cu3N thin film layers were studied by UV-MIR (0.2–40 μm) ellipsometry and optical transmission, to be able to achieve the goal of a low-cost absorber material to replace the conventional silicon. The reactive-RF-sputtered Cu3N films were also investigated by focused ion beam scanning electron microscopy and both FTIR and Raman spectroscopies. The less dense layer was found to have a value of the static refractive index of 2.304, and the denser film had a value of 2.496. The iso-absorption gap, E04, varied between approximately 1.3 and 1.8 eV and could be considered suitable as a solar light absorber.

CdZnTe-based detectors are highly valued because of their high spectral resolution, which is an essential feature for nuclear medical imaging. However, this resolution is compromised when there are substantial defects in the CdZnTe crystals. In this study, we present a learning-based approach to determine the spatially dependent bulk properties and defects in semiconductor detectors. This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. In addition, a sensitivity analysis was performed to determine the effect of noisy data on the accuracy of the model.

We provide an overview of our novel single-shot Time-of-Flight camera concept, based on Synthetic-Wavelength-Interferometry. The introduced prototype allows for dynamic 3D reconstructions with high depth precision.

Room-temperature semiconductor radiation detectors (RTSD) such as CdZnTe are popular in Computed Tomography (CT) imaging and other applications. Transport properties and material defects with respect to electron and hole transport often need to be characterized, which is a labor intensive process. However, these defects often vary from one RTSD to another and are not known a priori during characterization of the material. In recent years, physics-inspired machine learning (PI-ML) models have been developed for the RTSDs which have the ability to characterize the defects in a RTSD by discretizing it volumetrically. These learning models capture the heterogeneity of the defects in the RTSD—which arises due to the fabrication process and the energy bands of elements in the RTSD. In those models, the different defects of RTSD—trapping, detrapping and recombination for electrons and holes—are present. However, these defects are often unknown. In this work, we show the capabilities of a PI-ML model which has been developed considering all the material defects to identify certain defects which are present (or absent). Additionally, these models can identify the defects over the volume of the RTSD in a discretized manner.

Room-temperature semiconductor radiation detectors (RTSD) have broad applications in medical imaging, homeland security, astrophysics and others. RTSDs such as CdZnTe, CdTe are often pixelated, and characterization of these detectors at micron level can benefit 3-D event reconstruction at sub-pixel level. Material defects alongwith electron and hole charge transport properties need to be characterized which requires several experimental setups and is labor intensive. The current state-of-art approaches characterize each detector pixel, considering the detector in bulk. In this article, we propose a new microscopic learning-based physical models of RTSD based on limited data compared to what is dictated by the physical equations. Our learning models uses a physical charge transport considering trapping centers. Our models learn these material properties in an indirect manner from the measurable signals at the electrodes and/or free and/or trapped charges distributed in the RTSD for electron-hole charge pair injections in the material. Based on the amount of data used during training our physical model, our algorithm characterizes the detector for charge drifts, trapping, detrapping and recombination coefficients considering multiple trapping centers or as a single equivalent trapping center. The RTSD is segmented into voxels spatially, and in each voxel, the material properties are modeled as learnable parameters. Depending on the amount of data, our models can characterize the RTSD either completely or in an equivalent manner.