Related papers: Process, Bias and Temperature Scalable CMOS Analog…
Cooling high-power electronics in multilayer integrated circuits (ICs) is challenging for existing cooling methods. In this work, we designed through-chip microchannels (TCMCs) that cross the entire chip perpendicularly to the layers, with…
Content Addressable Memories (CAMs) are considered a key-enabler for in-memory computing (IMC). IMC shows order of magnitude improvement in energy efficiency and throughput compared to traditional computing techniques. Recently, analog CAMs…
Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new…
When designing and studying circuits operating at cryogenic temperatures understanding local heating within the circuits is critical due to the temperature dependence of transistor and noise behavior. We have investigated local heating…
The quest for energy-efficient, scalable neuromorphic computing has elevated compute-in-memory (CIM) architectures to the forefront of hardware innovation. While memristive memories have been extensively explored for synaptic implementation…
Linear transformations are cornerstone operations utilized in modern computing, but are computationally expensive on current electronic platforms. Optical computing has been positioned as a new computing solution, promising high speed and…
Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic…
Silicon-based quantum computing has the potential advantages of low cost, high integration density, and compatibility with CMOS technologies. The detuning mechanism has been used to experimentally achieve silicon two-qubit quantum gates and…
Recent advances in artificial intelligence, coupled with increasing data bandwidth requirements, in applications such as video processing and high-resolution sensing, have created a growing demand for high computational performance under…
Analog/Mixed-Signal (AMS) circuits and systems continually present significant challenges to designers with the increase of design complexity and aggressive technology scaling. This is due to the large number of design factors and…
The miniaturization of transistors down to 5nm and beyond, plus the increasing complexity of integrated circuits, significantly aggravate short channel effects, and demand analysis and optimization of more design corners and modes.…
The superior density of passive analog-grade memristive crossbars may enable storing large synaptic weight matrices directly on specialized neuromorphic chips, thus avoiding costly off-chip communication. To ensure efficient use of such…
Analog Compute-in-Memory (CiM) accelerators use analog-digital converters (ADCs) to read the analog values that they compute. ADCs can consume significant energy and area, so architecture-level ADC decisions such as ADC resolution or number…
Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The…
We have designed, fabricated and operated a scalable system for applying independently programmable time-independent, and limited time-dependent flux biases to control superconducting devices in an integrated circuit. Here we report on the…
Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) crossbar, ReRAM-based accelerator shows potential in accelerating…
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of…
Quantum computing will change the way we tackle certain problems. It promises to dramatically speed-up many chemical, financial, and machine-learning applications. However, to capitalize on those promises, complex design flows composed of…
This study maps the quantum landscape of superconducting diodes (SDs) \cite{nadeem23} onto the quantum technology architecture, which is currently constrained by fundamental challenges in control and scalability. In the existing…
Controlled atomic scale fabrication of functional devices is one of the holy grails of nanotechnology. The most promising class of techniques that enable deterministic nanodevice fabrication are based on scanning probe patterning or surface…