End-to-End Industrial Study of Retiming

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Rapid single-flux quantum (RSFQ) is one of the most advanced and promising superconducting logic families, offering exceptional energy efficiency and speed. RSFQ technology requires delay registers (DFFs) and splitter cells to satisfy the path-balancing an ...

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

The engineering of tin halide perovskites has led to the development of p-type transistors with field-effect mobilities of over 70 cm2 V-1 s-1. However, due to their background hole doping, these perovskites are not suitable for n-type transistors. Ambipol ...

Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

Mechanical resonators are widely used in sensors, transducers and optomechanical systems, where mechanical dissipation sets the ultimate limit to performance. Over the past 15 years, the quality factors in strained mechanical resonators have increased by f ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

The COVID-19 pandemic has led to a significant increase in working from home worldwide, making the workfrom-home (WFH) setting a crucial context for studying the influence of indoor environmental quality (IEQ) on workers' well-being and productivity. A nar ...

At room temperature, mechanical motion driven by the quantum backaction of light has been observed only in pioneering experiments in which an optical restoring force controls the oscillator stiffness1,2. For solid-state mechanical resonators in which oscil ...

Factored form is a powerful multi-level representation of a Boolean function that readily translates into an implementation of the function in CMOS technology. In particular, the number of literals in a factored form correlates strongly with the number of ...

The digital revolution has significantly transformed our world over the past decades, driven by the scaling of transistor dimensions and the exponential increase in computation power. However, as the CMOS scaling era approaches its end, the semiconductor i ...

Monolithic pixel sensors integrate the sensor matrix and readout in the same silicon die, and therefore present several advantages over the more largely used hybrid detectors in high-energy physics. They offer an easier detector assembly, lower cost, lower ...

There is a never-ending push for electronic systems to provide faster operation speeds, higher energy efficiencies, and higher power capabilities at smaller scales. These requirements are apparent in different areas of electronics, from radiofrequency (RF) ...

With Moore's law coming to an end, increasingly more hope is being put in specialized hardware implemented on reconfigurable architectures such as Field-Programmable Gate Arrays (FPGAs). Yet, it is often neglected that these architectures themselves experi ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

Organic electrochemical transistors (OECTs) have gained enormous attention due to their potential for bioelectronics and neuromorphic computing. However, their implementation into real-world applications is still impeded by a lack of understanding of the c ...

The integration of technology in the medical field has greatly improved accuracy in diagnoses, thus leading to more effective treatments. Wearable and implantable medical devices offer great potential for remote patient monitoring, particularly for heart f ...

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively m ...

End-to-End Industrial Study of Retiming

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Algebraic and Boolean Methods for SFQ Superconducting Circuits

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Graph generative deep learning models with an application to circuit topologies

Strain relaxation and multidentate anchoring in n-type perovskite transistors and logic circuits

Technology-Aware Logic Synthesis for Superconducting Electronics

Extensions of Peer Prediction Incentive Mechanisms

Ultrahigh-quality-factor micro- and nanomechanical resonators using dissipation dilution

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

A state-of-the-art, systematic review of indoor environmental quality studies in work-from-home settings

Room-temperature quantum optomechanics using an ultralow noise cavity

Improving Standard-Cell Design Flow using Factored Form Optimization

Energy-Efficient Electronic Functions Based on the Co-integration of 2D and Ferroelectric Materials

Front-End Circuits for Radiation-Hard Monolithic CMOS Sensors targeting High-Energy Physics Applications

New ultrahigh-speed device concepts: from THz nanoplasma devices to glass-like electronics for neuromorphic computation

Automating the Design of Programmable Interconnect for Reconfigurable Architectures

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Unraveling the Electrochemical Electrode Coupling in Integrated Organic Electrochemical Transistors

Integrated Electronics for Deep Implants to Remotely Monitor Hemodynamics

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives