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The integration of high-performance n-type and p-type two-dimensional transistors — which can be fabricated on 300 mm wafers using a die-by-die transfer process — is an important step in the lab-to-fab transition of two-dimensional semiconductors.
A 3D stackable computing-in-memory array that is based on resistive random-access memory could accelerate the implementation of machine learning algorithms.
The monolithic integration of photonic and electronic technology can be used to create miniaturized implantable microsystems capable of high-resolution optical neural control and electrical recording in deep brain regions.
An effective gate voltage doping method can be used to create single-gate molybdenum ditelluride field-effect transistors that can be reconfigured between rectification, memory, logic and neuromorphic functions.
Dual-gate heterojunction transistors that are based on monolayer molybdenum disulfide and carbon nanotubes can provide tunable Gaussian and sigmoid functions for support vector machine computing.
Event-driven, in-sensor computing can be performed by individual vision sensors composed of two parallelly connected photodiodes, enabling vision recognition of dynamic motion.
Ring oscillator circuits that operate at gigahertz frequencies and are based on monolayer molybdenum disulfide can be created with the help of a design–technology co-optimization approach.
A silicon photonics modulator design approach is proposed, in which the inductive networks and termination resistors are designed in conjunction with the optical phase shifter. A complementary metal–oxide–semiconductor (CMOS) silicon photonics transmitter developed with this approach achieved 112 gigabaud transmission with an energy efficiency better than 1 pJ per bit.
The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.
This Review provides a full-spectrum classification of computing-in-memory technologies by identifying the degree of memory cells participating in the computation as inputs and/or output, creating a platform for comparing the advantages and disadvantages of each of the different technologies.
An adhesive bioelectronic patch that can conform to irregular curvilinear surfaces can be used in vivo to stimulate the heart and record electrocardiograms of freely moving rats.
External-magnetic-field-free switching of the perpendicular magnetic anisotropy in magnetic layers is a prerequisite for the wide adoption of spintronic devices. This challenge could be met by the Weyl semimetal TaIrTe4, which is now shown to generate an out-of-plane polarized spin current at room temperature.
A network of coupled electronic oscillators can be engineered to find ground states of Ising Hamiltonians and solve various combinatorial optimization problems.
Machine-learning-driven atomistic simulations are shown to describe phase-change materials on the length scale of real devices. This demonstration suggests that the atomic-scale design of phase-change architectures, programming conditions and full devices could be within reach.
By monolithically integrating organic light-emitting diodes (OLEDs) with complementary metal–oxide–semiconductor (CMOS) technology, implantable optogenetic probes can be created to selectively address individual neurons.
An autonomous wearable device that is capable of monitoring sweat for extended periods of time could help collect data for the development of personalized medicine.
