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With the increased attention to memristive-based in-memory analog computing (IMAC) architectures as an alternative for energy-hungry computer systems for data-intensive applications, a tool that enables exploring their device- and…
Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually…
Scanning Electron Microscopes (SEMs) are widely renowned for their ability to analyze the surface structures of microscopic objects, offering the capability to capture highly detailed, yet only grayscale, images. To create more expressive…
Scalable solid-state quantum computers will require integration with analog and digital electronics. Efficiently simulating the quantum-classical electronic interface is hence of paramount importance. Here, we present Verilog-A compact…
If modern computers are sometimes superior to humans in some specialized tasks such as playing chess or browsing a large database, they can't beat the efficiency of biological vision for such simple tasks as recognizing and following an…
High-efficiency micro-light-emitting diodes (Micro-LEDs) are key devices for next-generation display technology. However, when the mesa size is reduced to around tens of micrometers or less, the luminous efficiency is constrained by the…
Scanning Transmission Electron Microscopy (STEM) is a critical tool for imaging the properties of materials and biological specimens at atomic scale, yet our understanding of relevant electron beam damage mechanisms is incomplete. Recent…
The deployment of dense, low-cost sensors is critical for realizing ubiquitous smart environments. However, existing sensing solutions struggle with the energy, scalability, and reliability trade-offs imposed by battery maintenance,…
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Based on simplified one-dimensional steady-state analysis of thermoelectric phenomena and on analogies between thermal and electrical domains, we propose both lumped and distributed parameter electrical models for thermoelectric devices.…
Memory circuit elements, namely memristive, memcapacitive and meminductive systems, are gaining considerable attention due to their ubiquity and use in diverse areas of science and technology. Their modeling within the most widely used…
We present an optimized conductance-based retina microcircuit simulator which transforms light stimuli into a series of graded and spiking action potentials through photo transduction. We use discrete retinal neuron blocks based on a…
Monolithic active pixel sensors (MAPS) produced in a 65 nm CMOS imaging technology are being investigated for applications in particle physics. The MAPS design has a small collection electrode characterized by an input capacitance of ~fF,…
Understanding of the electroluminescence (EL) mechanism in optoelectronic devices is important for further optimization of their efficiency and effectiveness. Here, a quantum mechanical approach is formulated for modeling EL processes in…
Atom spin sensors occupy a prominent position in the scenario of quantum technology, as they can combine precise measurements with appealing miniature packages which are crucial for many applications. In this work, we report on the design…
We provide a computational method for quickly determining the correct distribution of optically active nanoparticles for a desired response. This is achieved by simulating the optical response of single nanoparticles and performing a…
Inverse rendering pipelines are gaining prominence in realizing photo-realistic reconstruction of real-world objects for emulating them in virtual reality scenes. Apart from material reflectances, spectral rendering and in-scene…
Modern surgical systems increasingly rely on intelligent scene understanding to improve intra-operative safety and situational awareness, with surgical scene segmentation playing a fundamental role in fine-grained surgical perception.…
Integrated quantum photonics requires compact, efficient, and low-power phase modulators. While silicon nitride (SiN) is a promising platform, existing modulators suffer from high power consumption, thermal crosstalk, or high driving…
We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding much more sampling of chemical space and more data on…