As the demand for digital services grows, so does the need for data centres and transmission networks. Unfortunately, these data systems consume vast amounts of energy, resulting in nearly 1% of all energy-related greenhouse gas emissions. This project aims to invent novel quantum devices for highly energy-efficient computing that may help reduce the global digital carbon footprint. Tellurium (Te)-based devices will be gated through antiferroelectric (AFE) stacks to form a multi-valued-logic quantum device. A tapered Te region will be used as the active material of the developed transistors. This proposed architecture can rely on quantum tunnelling effects to minimize energy consumption per transition while circumventing the limitations of the classical field-effect transistors. The AFE layer can transform binary logic switches into ternary logic devices, allowing fewer transistors to perform the same function and reduce overall power consumption. The researchers will first develop, calibrate, and validate an AFE model and use the model to investigate the characteristics of AFE capacitors. The electronic states and materials parameters of Te will also be explored. Next, a new simulation tool will be developed to study the physics related to the proposed devices and the optimal device structure will be proposed for a prototype. The modelling results will be further validated and calibrated against experiments, allowing the device to be updated iteratively for further optimization. The quantum simulation tool and prototype ternary devices will not only help build ultra-low-power electronics for sustainable computing but will also elevate our knowledge in material science, quantum physics, and electronics.
Figure 1. (a) Tellurium crystal structure with unique helical chains for the active channel material of the device. (b) A double hysteresis loop in the polarization vs. electric field characteristic of antiferroelectric thin film for multi-valued-logic operation.
Related Content
Building Blocks for Quantum Neuromorphic Computing: Superconducting Quantum Memcapacitors
Quantum neuromorphic computing (QNC) is a novel method that combines quantum computing with brain-inspired neuromorphic computing. Neuromorphic computing performs computations using a complex ensemble of artificial neurons and synapses (i.e., electrical circuits) to emulate the human brain. QNC may lead to a quantum advantage by realizing these components with quantum memory elements, or memelements, which […]
June 12, 2023
Silicon Platform for Electron Spin Qubits
Summary Scaling solid-state quantum processors to a useful threshold while maintaining the requisite precision in quantum control remains a challenge. We propose a quantum metal-oxide-semiconductor (QMOS) architecture operating at cryogenic temperatures that is based on a network/node approach as a means to scalability. By working with QMOS, we benefit from the deep investments and […]
December 7, 2018
Plasmon Control of Quantum States in Semiconductor Nanocrystals
Summary Thanks to the light-induced collective oscillations of free charges at the boundary between a conducting material and a dielectric, known as surface plasmon resonance, metallic nanostructures can exhibit strong light absorption and scattering. The sensitivity of these resonances to the local environment and shape of the metallic structures allows them to be used, […]
March 21, 2018
Free-space Polarization-selective Microcavity based on Chiral Metasurfaces
Summary Developing a new type of Fabry-Pérot cavity that allows improved control of the atoms’ emission into the cavity mode will result in enhancement of the efficiency and fidelity of quantum state transfer from photons to atoms and back. This in turn can be used to improve the performance of quantum networks and repeaters, as […]
September 19, 2019