arXiv subjects

Edwin Mayes

Publications and source records attributed to Edwin Mayes.

4 recordsLinked to original sources

arXiv2025

The properties of the nitrogen-vacancy center in milled chemical vapor deposition nanodiamonds

Fluorescent nanodiamonds (FNDs) containing negatively charged nitrogen-vacancy (NV-) centers are vital for many emerging quantum sensing applications from magnetometry to intracellular sensing in biology. However, developing a scalable fabrication method for FNDs hosting color centers with consistent bulk-like photoluminescence (PL) and spin coherence properties remains a highly desired but unrealized goal. Here, we investigate optimized ball milling of single-crystal diamonds produced via chemical vapor deposition (CVD) and containing 2 ppm of substitutional nitrogen and 0.3 ppm of NV- to achieve this goal. The NV charge state, PL lifetime, and spin properties of bulk CVD diamond samples are directly compared to milled CVD FNDs and commercial high-pressure high-temperature (HPHT) FNDs. We find that on average, the relative contribution of the NV- charge state to the total NV PL is lower and the NV PL lifetime is longer in CVD FNDs compared to HPHT FNDs, both likely due to the lower Ns0 concentration in CVD FNDs. The CVD bulk and CVD FNDs on average show similar average T1 spin relaxation times of 3.2 $\pm$ 0.7 ms and 4.7 $\pm$ 1.6 ms, respectively, compared to 0.17 $\pm$ 0.01 ms for commercial HPHT FNDs. Our results demonstrate that ball milling of CVD diamonds enables the large-scale fabrication of NV ensembles in FNDs with bulk-like T1 spin relaxation properties.

cond-mat.mes-hall↗

arXiv2022

Near-Surface Electrical Characterisation of Silicon Electronic Devices Using Focused keV Ions

The demonstration of universal quantum logic operations near the fault-tolerance threshold establishes ion-implanted near-surface donor atoms as a plausible platform for scalable quantum computing in silicon. The next technological step forward requires a deterministic fabrication method to create large-scale arrays of donors, featuring few hundred nanometre inter-donor spacing. Here, we explore the feasibility of this approach by implanting low-energy ions into silicon devices featuring an enlarged 60x60 $\mu$m sensitive area and an ultra-thin 3.2 nm gate oxide - capable of hosting large-scale donor arrays. By combining a focused ion beam system incorporating an electron-beam-ion-source with in-vacuum ultra-low noise ion detection electronics, we first demonstrate a versatile method to spatially map the device response characteristics to shallowly implanted 12 keV $^1$H$_2^+$ ions. Despite the weak internal electric field, near-unity charge collection efficiency is obtained from the entire sensitive area. This can be explained by the critical role that the high-quality thermal gate oxide plays in the ion detection response, allowing an initial rapid diffusion of ion induced charge away from the implant site. Next, we adapt our approach to perform deterministic implantation of a few thousand 24 keV $^{40}$Ar$^{2+}$ ions into a predefined micro-volume, without any additional collimation. Despite the reduced ionisation from the heavier ion species, a fluence-independent detection confidence of $\geq$99.99% was obtained. Our system thus represents not only a new method for mapping the near-surface electrical landscape of electronic devices, but also an attractive framework towards mask-free prototyping of large-scale donor arrays in silicon.

cond-mat.mes-hall↗

arXiv2020

Deterministic Single Ion Implantation with 99.87% Confidence for Scalable Donor-Qubit Arrays in Silicon

The attributes of group-V-donor spins implanted in an isotopically purified $^{28}$Si crystal make them attractive qubits for large-scale quantum computer devices. Important features include long nuclear and electron spin lifetimes of $^{31}$P, hyperfine clock transitions in $^{209}$Bi and electrically controllable $^{123}$Sb nuclear spins. However, architectures for scalable quantum devices require the ability to fabricate deterministic arrays of individual donor atoms, placed with sufficient precision to enable high-fidelity quantum operations. Here we employ on-chip electrodes with charge-sensitive electronics to demonstrate the implantation of single low-energy (14 keV) P$^+$ ions with an unprecedented $99.87\pm0.02$% confidence, while operating close to room-temperature. This permits integration with an atomic force microscope equipped with a scanning-probe ion aperture to address the critical issue of directing the implanted ions to precise locations. These results show that deterministic single-ion implantation can be a viable pathway for manufacturing large-scale donor arrays for quantum computation and other applications.

cond-mat.mes-hall↗

arXiv2018

High order synaptic learning in neuro-mimicking resistive memories

Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, an electroforming-free and complementary metal oxide semiconductor (CMOS)-compatible memristor based on oxygen-deficient SrTiO$_{3-x}$ (STO$_x$) is reported to imitate synaptic learning rules. Through spectroscopic and cross-sectional transmission electron microscopic analyses, electroforming-free characteristics are attributed to the bandgap reduction of STO$_x$ by the formation of oxygen vacancies. The potential of such memristors to behave as artificial synapses is demonstrated by successfully implementing high order time- and rate-dependent synaptic learning rules. Also, a simple hybrid CMOS-memristor approach is presented to implement a variety of synaptic learning rules. Results are benchmarked against biological measurements form hippocampal and visual cortices with good agreement. This demonstration is a step towards the realization of large scale adaptive neuromorphic computation and networks.

cond-mat.dis-nn↗