Soft-Error Tolerant Quasi Delay-insensitive Circuits

A hard error is an error that damages a circuit irrevocably; a soft error flips the logic states without causing any physical damage to the circuit, resulting in transient corruption of data. They result in transient, inconsistent corruption of data. The soft-error tolerance of logic circuits is recently getting more attention, since the soft- error rate of advanced CMOS devices is higher than before. As a response to the concern on soft errors, we propose a new method for making asynchronous circuits tolerant to soft errors. Since it relies on a property unique to asynchronous circuits, the method is different from what is done in synchronous circuits with triple modular redundancy. Asynchronous circuits have been attractive to the designers of reliable systems, because of their clock-less design, which makes them more robust to variations on computation time of modules. The quasi delay-insensitive (QDI) design style is one of the most robust asynchronous design styles for general computation; it makes one minimal assumption on delays in gates and wires. QDI circuits are easy to verify, simple, and modular, because the correct operation of a QDI circuit is independent of delays in gates and wires. Here, we shall overview how to design a QDI circuit, and what will happen if a soft error occurs on a QDI circuit. Then the crucial components of the method are shown: (1) a special kind of duplication for random logic (when each bit has to be corrected individually), (2) special protection circuitry for arbiter and synchronizer (as needed for example for external interrupts), (3) reconfigurable circuits using a special configuration unit, and (4) error correcting for memory arrays and other structures in which the data bits can be self- corrected. The solution of protecting random logic is compared with alternatives, which use other types of error correcting codes (e.g., parity code) in a QDI circuit. It turns out that the duplication generates efficient circuits more commonly than other possible constructions. Finally, the design of a soft-error tolerant asynchronous microprocessor is detailed and testing results of the soft-error tolerance of the microprocessor are shown.</p

Coherence of a field-gradient-driven singlet-triplet qubit coupled to many-electron spin states in 28Si/SiGe

Engineered spin-electric coupling enables spin qubits in semiconductor nanostructures to be manipulated efficiently and addressed individually. While synthetic spin-orbit coupling using a micromagnet is widely used for driving qubits based on single spins in silicon, corresponding demonstration for encoded spin qubits is so far limited to natural silicon. Here, we demonstrate fast singlet-triplet qubit oscillation (~100 MHz) in a gate-defined double quantum dot in $^{28}$Si/SiGe with an on-chip micromagnet with which we show the oscillation quality factor of an encoded spin qubit exceeding 580. The coherence time $\textit{T}_{2}$* is analyzed as a function of potential detuning and an external magnetic field. In weak magnetic fields, the coherence is limited by fast noise compared to the data acquisition time, which limits $\textit{T}_{2}$* < 1 ${\mu}$s in the ergodic limit. We present evidence of sizable and coherent coupling of the qubit with the spin states of a nearby quantum dot, demonstrating that appropriate spin-electric coupling may enable a charge-based two-qubit gate in a (1,1) charge configuration

Strong hole-photon coupling in planar Ge: probing the charge degree and Wigner molecule states

Semiconductor quantum dots (QDs) in planar germanium (Ge) heterostructures have emerged as frontrunners for future hole-based quantum processors. Notably, the large spin-orbit interaction of holes offers rapid, coherent electrical control of spin states, which can be further beneficial for interfacing hole spins to microwave photons in superconducting circuits via coherent charge-photon coupling. Here, we present strong coupling between a hole charge qubit, defined in a double quantum dot (DQD) in a planar Ge, and microwave photons in a high-impedance ($Z_\mathrm{r} = 1.3 ~ \mathrm{k}\Omega$) superconducting quantum interference device (SQUID) array resonator. Our investigation reveals vacuum-Rabi splittings with coupling strengths up to $g_{0}/2\pi = 260 ~ \mathrm{MHz}$, and a cooperativity of $C \sim 100$, dependent on DQD tuning, confirming the strong charge-photon coupling regime within planar Ge. Furthermore, utilizing the frequency tunability of our resonator, we explore the quenched energy splitting associated with strongly-correlated Wigner molecule (WM) states that emerge in Ge QDs. The observed enhanced coherence of the WM excited state signals the presence of distinct symmetries within related spin functions, serving as a precursor to the strong coupling between photons and spin-charge hybrid qubits in planar Ge. This work paves the way towards coherent quantum connections between remote hole qubits in planar Ge, required to scale up hole-based quantum processors.Comment: 22 pages, 12 figure

Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)-which has high temporal, spatial, and spectral resolutions-is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7-589.6, 603.6-631.8, 641.2-655.35, 664.8-679.0, 698.0-712.3, and 731.4-784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir