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An ever-increasing demand for high-performance silicon sensors requires complex sensor designs that are challenging to simulate and model. The combination of electrostatic finite element simulations with a transient Monte Carlo approach…
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in…
High frequency performance limits of graphene field-effect transistors (FETs) down to a channel length of 20nm are examined by using self-consistent quantum simulations. The results indicate that although Klein band-to-band tunneling is…
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion"…
This work presents the development of an electronic system for the electrical characterization of pH sensors based on the extended gate field effect transistor (EGFET). We designed an electronic circuit with a microcontroller (PIC15F14K50)…
In the past decade, detection of THz radiation by plasma-wave-assisted frequency mixing in antenna-coupled field-effect transistors (TeraFETs) -- implemented in various semiconductor material systems (Si CMOS, GaN/AlGaN, GaAs/AlGaAs,…
Emerging low-dimensional nanomaterials have been studied for decades in device applications as field-effect transistors (FETs). However, properly reporting and comparing device performance has been challenging due to the involvement and…
Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly developed diffusion model shows great potential. However, existing…
The key to device-edge co-inference paradigm is to partition models into computation-friendly and computation-intensive parts across the device and the edge, respectively. However, for Graph Neural Networks (GNNs), we find that simply…
It has become a popular paradigm to transfer the knowledge of large-scale pre-trained models to various downstream tasks via fine-tuning the entire model parameters. However, with the growth of model scale and the rising number of…
The progenitors for many types of supernovae (SNe) are still unknown, and an approach to diagnose their physical origins is to investigate the light curve brightness and shape of a large set of SNe. However, it is often difficult to compare…
Molecular dynamics simulations at a constant electric potential are an essential tool to study electrochemical processes, providing microscopic information on the structural, thermodynamic, and dynamical properties. Despite the numerous…
Graphene provides a fascinating testbed for new physics and exciting opportunities for future applications based on quantum phenomena. To understand the coherent flow of electrons through a graphene device, we employ a nanoscale probe that…
Graph transformers have gained popularity in various graph-based tasks by addressing challenges faced by traditional Graph Neural Networks. However, the quadratic complexity of self-attention operations and the extensive layering in graph…
we have fabricated transparent electronic devices based on graphene materials with thickness down to one single atomic layer by the transfer printing method. The resulting printed graphene devices retain high field effect mobility and have…
Test-Time Adaptation (TTA) aims to enhance the generalization of deep learning models when faced with test data that exhibits distribution shifts from the training data. In this context, only a pre-trained model and unlabeled test data are…
Tri-gate ferroelectric FETs with Hf0.5Zr0.5O2 gate insulator for memory and neuromorphic applications are fabricated and characterized for multi-level operation. The conductance and threshold voltage exhibit highly linear and symmetric…
Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the…
This article presents an open-source Python package for simulating micro-thermoelectric generators, based on the work by D. Beretta et al. (Sustainable Energy Fuels, 2017). Featuring a user-friendly graphical user interface and robust…
Tunneling field-effect transistors (TFETs) have gained a great deal of recent interest due to their potential to reduce power dissipation in integrated circuits. One major challenge for TFETs so far has been achieving high drive currents,…