Circuit Complexity Research Articles

Measurement of cryoelectronics heating using a local quantum dot thermometer in silicon

Silicon technology offers the enticing opportunity for monolithic integration of quantum and classical electronic circuits. However, the power consumption levels of classical electronics may compromise the local chip temperature and hence the fidelity of qubit operations. Here, we utilize a quantum-dot-based thermometer embedded in an industry-standard silicon field-effect transistor (FET), to assess the local temperature increase produced by an active FET placed in close proximity. We study the impact of both static and dynamic operation regimes. When the FET is operated statically, we find a power budget of 45 nW at 100 nm separation whereas at 216 μm the power budget raises to 150 μW. When operated dynamically, we observe negligible temperature increase for the switch frequencies tested up to 10 MHz. Our work describes a method to accurately map out the available power budget at a distance from a solid-state quantum processor and indicate under which conditions cryoelectronics circuits may allow the operation of hybrid quantum-classical systems.

Shaping the olfactory map: cell type-specific activity patterns guide circuit formation.

The brain constructs spatially organized sensory maps to represent sensory information. The formation of sensory maps has traditionally been thought to depend on synchronous neuronal activity. However, recent evidence from the olfactory system suggests that cell type-specific temporal patterns of spontaneous activity play an instructive role in shaping the olfactory glomerular map. These findings challenge traditional views and highlight the importance of investigating the spatiotemporal dynamics of neural activity to understand the development of complex neural circuits. This review discusses the implications of new findings in the olfactory system and outlines future research directions.

Progress in Spin Logic Devices Based on Domain-Wall Motion.

Spintronics, utilizing both the charge and spin of electrons, benefits from the nonvolatility, low switching energy, and collective behavior of magnetization. These properties allow the development of magnetoresistive random access memories, with magnetic tunnel junctions (MTJs) playing a central role. Various spin logic concepts are also extensively explored. Among these, spin logic devices based on the motion of magnetic domain walls (DWs) enable the implementation of compact and energy-efficient logic circuits. In these devices, DW motion within a magnetic track enables spin information processing, while MTJs at the input and output serve as electrical writing and reading elements. DW logic holds promise for simplifying logic circuit complexity by performing multiple functions within a single device. Nevertheless, the demonstration of DW logic circuits with electrical writing and reading at the nanoscale is still needed to unveil their practical application potential. In this review, we discuss material advancements for high-speed DW motion, progress in DW logic devices, groundbreaking demonstrations of current-driven DW logic, and its potential for practical applications. Additionally, we discuss alternative approaches for current-free information propagation, along with challenges and prospects for the development of DW logic.

Integrated Photonic Passive Building Blocks on Silicon-On-Insulator Platform

Integrated photonics on Silicon-On-Insulator (SOI) substrates is a well developed research field that has already significantly impacted various fields, such as quantum computing, micro sensing devices, biosensing, and high-rate communications. Although quite complex circuits can be made with such technology, everything is based on a few ’building blocks’ which are then combined to form more complex circuits. This review article provides a detailed examination of the state of the art of integrated photonic building blocks focusing on passive elements, covering fundamental principles and design methodologies. Key components discussed include waveguides, fiber-to-chip couplers, edges and gratings, phase shifters, splitters and switches (including y-branch, MMI, and directional couplers), as well as subwavelength grating structures and ring resonators. Additionally, this review addresses challenges and future prospects in advancing integrated photonic circuits on SOI platforms, focusing on scalability, power efficiency, and fabrication issues. The objective of this review is to equip researchers and engineers in the field with a comprehensive understanding of the current landscape and future trajectories of integrated photonic components on SOI substrates with a 220 nm thick device layer of intrinsic silicon.

Protocol for the establishment of a serine integrase-based platform for functional validation of genetic switch controllers in eukaryotic cells.

Serine integrases (Ints) are a family of site-specific recombinases (SSRs) encoded by some bacteriophages to integrate their genetic material into the genome of a host. Their ability to rearrange DNA sequences in different ways including inversion, excision, or insertion with no help from endogenous molecular machinery, confers important biotechnological value as genetic editing tools with high host plasticity. Despite advances in their use in prokaryotic cells, only a few Ints are currently used as gene editors in eukaryotes, partly due to the functional loss and cytotoxicity presented by some candidates in more complex organisms. To help expand the number of Ints available for the assembly of more complex multifunctional circuits in eukaryotic cells, this protocol describes a platform for the assembly and functional screening of serine-integrase-based genetic switches designed to control gene expression by directional inversions of DNA sequence orientation. The system consists of two sets of plasmids, an effector module and a reporter module, both sets assembled with regulatory components (as promoter and terminator regions) appropriate for expression in mammals, including humans, and plants. The complete method involves plasmid design, DNA delivery, testing and both molecular and phenotypical assessment of results. This platform presents a suitable workflow for the identification and functional validation of new tools for the genetic regulation and reprogramming of organisms with importance in different fields, from medical applications to crop enhancement, as shown by the initial results obtained. This protocol can be completed in 4 weeks for mammalian cells or up to 8 weeks for plant cells, considering cell culture or plant growth time.

A novel design of layered recurrent neural networks for fractional order Caputo–Fabrizio stiff electric circuit models

Electrical engineering models often rely on complex circuit configurations that facilitate the dynamic flow of electrically charged particles within a closed conductive network. These circuits serve as essential tools for simulating and analyzing diverse electrical systems and components. This paper introduces a study on nonlinear fractional circuits modeling through the development of a stochastic neuro-computational artificial intelligent-based solver to address mathematical models governing the Fractional order Caputo–Fabrizio stiff electric circuit model (FO-CFSECM) by manipulating the knacks of layered recurrent neural networks (LRNNs) trained with Gradient-based local search algorithm (GLA). In fractional calculus, the Caputo–Fabrizio (CF) fractional order derivative (FOD) emerges as a powerful instrument, binding its capabilities to deliver remarkably accurate solutions for fractional stiff systems. The objective of this work is to exploit the numerical treatment comprehensively for the dynamics of fractal Resistor–Capacitor (RC) and fractal Resistor–Inductor (RL) circuit models by introducing the CF fractional operator. Through the application of artificial intelligence-based soft computing and advanced back-propagative deep neural networks, a deeper understanding of the behavior and distinctive characteristics inherent in these models is sought. The Levenberg–Marquardt optimizer serves as an efficient training GLA tool for learning of LRNNs weights of fractal RL/RC circuit models. The comparative studies on variants of FO-CFSECM demonstrate that LRNNs achieve an impressive mean square error (MSE) ranging from 10[Formula: see text] to 10[Formula: see text] and absolute error (AE) within 10[Formula: see text] to 10[Formula: see text]. The accuracy, reliability, and efficiency of LRNNs for solving the FO-CFSECM were further validated through MSE, AE, controlling parameters of state transitions, error histograms, and correlation measures.

Hydrogel-based radio frequency H2S sensor for in situ periodontitis monitoring and antibacterial treatment

Periodontitis, a chronic disease, can result in irreversible tooth loss and diminished quality of life, highlighting the significance of timely periodontitis monitoring and treatment. Meanwhile, hydrogen sulfide (H2S) in saliva, produced by pathogenic bacteria of periodontitis, is an important marker for periodontitis monitoring. However, the easy volatility and chemical instability of the molecule pose challenges to oral H2S sensing. Here, we report a wearable hydrogel-based radio frequency (RF) sensor capable of in situ H2S detection and antibacterial treatment. The RF sensor comprises an agarose hydrogel containing conjugated silver nanoparticles-chlorhexidine (AG-AgNPs-CHL hydrogel) integrated with split-ring resonators. Adhered to a tooth, the hydrogel-based RF sensor enables wireless transmission of sensing signals to a mobile terminal and a concurrent release of the broad-spectrum antibacterial agent chlorhexidine without complex circuits. With the selective binding of the AgNPs to the sulfidion, the RF sensor demonstrates good sensitivity, a wide detection range (2–30 μM), and a low limit of detection (1.2 μM). Compared with standard H2S measurement, the wireless H2S sensor can distinguish periodontitis patients from healthy individuals in saliva sample tests. The hydrogel-based wearable sensor will benefit patients with periodontitis by detecting disease-related biomarkers for practical oral health management.

Reversible Polarity Control in 2D MoTe2 Field‐Effect Transistors for Complementary Logic Gate Applications

AbstractPrecise control over polarity in field‐effect transistors (FETs) plays a pivotal role in the design and construction of complementary metal–oxide–semiconductor (CMOS) logic circuits. In particular, achieving such precise polarity control in 2D semiconductors is crucial for the further development of advanced electronic applications beyond unit devices. This paper presents a systematic investigation on the reversible transition of carrier types in a 2D MoTe2 semiconductor under different annealing atmospheres. Photoemission spectroscopy and density functional theory (DFT) calculations demonstrate that annealing processes in vacuum and in ambient air induce a modification in the density of states, resulting in alterations in p‐type or n‐type characteristics. These reversible changes are attributed to the physisorption and elimination of oxygen on the surface of MoTe2. Furthermore, it is found that the device geometry affects the polarity of the transistor. By strategically manipulating both the annealing conditions and the geometric configuration, the n‐ and p‐type unipolar characteristics of MoTe2 FETs are successfully modulated and ultimately demonstrating that the functionality of not only a complementary inverter with a high voltage gain of ≈20, but also more complex logic circuits of NAND and NOR gates.

Causal Unit Selection using Tractable Arithmetic Circuits

The unit selection problem aims to find objects, called units, that optimize a causal objective function which describes the objects' behavior in a causal context (e.g., selecting customers who are about to churn but would most likely change their mind if encouraged). While early studies focused mainly on bounding a specific class of counterfactual objective functions using data, more recent work allows one to find optimal units exactly by reducing the causal objective to a classical objective on a meta-model, and then applying a variant of the classical Variable Elimination (VE) algorithm to the meta-model---assuming a fully specified causal model is available. In practice, however, finding optimal units using this approach can be very expensive because the used VE algorithm must be exponential in the constrained treewidth of the meta-model, which is larger and denser than the original model. We address this computational challenge by introducing a new approach for unit selection that is not necessarily limited by the constrained treewidth. This is done through compiling the meta-model into a special class of tractable arithmetic circuits that allows the computation of optimal units in time linear in the circuit size. We finally present empirical results on random causal models that show order-of-magnitude speedups based on the proposed method for solving unit selection.

Empirical Investigation of a Single-phase Input Switched AC-DC Boost Converter with Improved Power Quality

The Boost converter topology is most widely used for power factor correction (PFC) converters. The conventional passive Boost PFC converter inherently suffers from the lack of controllability and non-ideal characteristics of the diode-bridge rectifier. Replacing diodes with active switch(es) offers viable solutions but increases the circuit complexity. Addressing this trade-off, a novel design of a single–phase input switched AC to DC Boost converter has been presented in this article. The active switch of the Boost DC converter is shifted to the input side of the full-bridge diode rectifier which provides more control on the input current without increasing much of the circuit complexity. Two Boost inductors and two output split capacitors are used for providing higher step-up operation compared to the conventional. The converter has been simulated both in open-loop and closed-loop conditions using the PSIM software. The simulation results show that the proposed converter provides good dynamic response and can maintain a constant stable output voltage during sudden load change. Furthermore, to evaluate the experimental performance of the suggested converter, a prototype converter has been developed. A scaled-down experiment has been performed which shows that the converter can achieve up to 85.2 % efficiency and an input power factor of 0.9.

Weak signal detection technique based on Durbin-Watson test and one-bit sampling.

Correlation-based detection techniques are widely used in the weak periodic signal detection field. Traditionally, they are based on extracting the correlation of a weak signal from noise. Considering the impact of a weak signal on the randomness of background noise, this article takes the opposite approach and proposes a weak signal detection technique based on the Durbin-Watson (DW) test and one-bit sampling, detecting the weak signal due to the extent to which the randomness of noise is affected. The randomness of noise is analyzed through the DW test, which is a method for detecting the randomness of data sequences through first-order autocorrelation. One-bit sampling is adopted to reduce the complexity of the sampling circuit and data processing algorithm. The effectiveness of the DW test in the situation of one-bit sampling is demonstrated through simulation and analysis. Simulation results show that the proposed technique is capable of detecting weak sinusoidal and square-wave signals with a signal-to-noise ratio (SNR) above -30 dB, and the frequency or SNR of a weak signal can be further estimated based on mutual constraints. The measured results confirm the capability. In addition, the factors of coherent sampling, noise bandwidth, and comparator threshold that influence the performance of the proposed technique are simulated and discussed in detail.

Transparent ionic liquid antenna based on multi-modes using multiple feeding structures

With the rapid development of modern wireless communication technology, there is an increasing demand for the functionality of the communication system carried by a single platform. To improve the utilization of the antenna, a multimode liquid antenna is proposed in this paper. The antenna is fabricated using 3D printing technology and mainly consists of a cylindrical dielectric resonator, a coaxial feeding structure, and a slot-coupling feeding structure. By hollowing out the middle of a resin container and filling it with low-loss ionic liquid with stable dielectric properties, a liquid dielectric resonator antenna (DRA) is formed. The various resonant modes (slot mode, HEM11δ, TM11δ, TM21δ, and TM31δ modes) can be excited by introducing different feeding structures. The operating frequencies and radiation patterns can be modulated by mode switching. Simulated and measured results show that the antenna operates in the frequency bands of 2.25–2.76 GHz (20.4 %) and 4.82–5.73 GHz (17.3 %), and the maximum gain can be up to 10.3 dBi. The proposed antenna structure avoids the complex RF control circuit design, which has various applications including 5G/6G communications, transparent electronic devices, and Internet of Things.

Logical characterizations of algebraic circuit classes over integral domains

AbstractWe present an adapted construction of algebraic circuits over the reals introduced by Cucker and Meer to arbitrary infinite integral domains and generalize the $\mathrm{AC}_{\mathbb{R}}$ and $\mathrm{NC}_{\mathbb{R}}^{}$ classes for this setting. We give a theorem in the style of Immerman’s theorem which shows that for these adapted formalisms, sets decided by circuits of constant depth and polynomial size are the same as sets definable by a suitable adaptation of first-order logic. Additionally, we discuss a generalization of the guarded predicative logic by Durand, Haak and Vollmer, and we show characterizations for the $\mathrm{AC}_{R}$ and $\mathrm{NC}_R^{}$ hierarchy. Those generalizations apply to the Boolean $\mathrm{AC}$ and $\mathrm{NC}$ hierarchies as well. Furthermore, we introduce a formalism to be able to compare some of the aforementioned complexity classes with different underlying integral domains.

EXERCISE IN ADULTS WITH ATTENTION-DEFICIT/HYPERACTIVITY DISORDER: A SYSTEMATIC REVIEW

INTRODUCTION & AIMS Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in adults. Stimulant medications are first-line treatments, they have many side effects and do not address common comorbidities such as cardiometabolic disease, depression, and anxiety. Exercise, by contrast, not only improves ADHD symptoms in youth but effectively treats such comorbidities, making it a potentially valuable treatment option. However, exercise efficacy in adults with ADHD has not been systematically reviewed. Therefore, we aimed to summarise the results of acute exercise and training interventions on health-related outcomes for adults with ADHD, including ADHD symptoms, cognitive function, and psychological well-being. METHODS Using PRISMA guidelines, fifteen databases were searched (including grey literature) on 27/08/2020, yielding 22,088 unique publications. Database alerts were created to capture additional studies (n= 2). Screening, data extraction, Risk of Bias and GRADE assessment was conducted by CEHB. RESULTS Seven acute (6 cycling, 1 self-selected, 1 yoga) and 6 training studies (Yoga, Pilates, Kickboxing, Tai Chi, General Coordination, Group Circuit Class, Cycling) were included. Among acute studies, those reporting small-to-moderate improvements in cognitive function were characterised by longer duration exercise (>30 minutes), withholding ADHD medication before exercise, and using cohorts with clinician-confirmed ADHD diagnosis. Among the training studies, only one reported an improvement in cognition following Pilates when compared to an inactive control. It was longer (6 months vs. <8 weeks), prescribed progressive intensity and volume, and had high adherence compared to the non-significant trials. The certainty of evidence was low or very low for all outcomes. CONCLUSIONS Overall, acute exercise may have a small positive effect on cognition in adults with ADHD; however, certainty of the evidence is low. Further research is needed to investigate the impact of different modalities, durations, and intensities of acute exercise and chronic exercise on adult ADHD.

Exact Computing Multiplier Design using 5-to-3 Counters for Image Processing

This work presents a novel approach to improve the area and energy efficiency of 5:3 counter, a key element used in digital arithmetic. To provide an effective substitute for addition operations, mostly in the partial product reduction stage of larger multipliers, this study suggests a new 5:3 counter. The Input Shuffling Unit (ISU) is employed within the proposed 5:3 counter to minimize gate-level implementation and path delay during partial product reduction in 16-bit and larger multipliers, thereby enhancing area and energy efficiency. Consequently, there are 84% fewer choices of input-output combinations, thereby decreasing the circuit complexity with respect to area and energy usage. When compared to its existing counterparts, the suggested 5:3 compressor improves area utilization and energy usage by an average of 11%, 17%, and 17% in 8-, 16-, and 32-bit multipliers, respectively. The results of simulations demonstrate the superiority of our method over traditional designs, providing an increase in both area and energy efficiency. These results highlight the applicability and scalability of our method, which is appropriate for a variety of applications such as embedded systems and digital signal processing.

Time-varying metasurface driven broadband radar jamming and deceptions

Conventional radar jamming and deception systems typically necessitate the custom design of complex circuits and algorithms to transmit an additional radio signal toward a detector. Consequently, they are often cumbersome, energy-intensive, and difficult to operate in broadband electromagnetic environment. With the ongoing trend of miniaturization of various devices and the improvement of radar system performance, traditional techniques no longer meet the requirements for broadband, seamless integration, and energy efficiency. Time-varying metasurfaces, capable of manipulating electromagnetic parameters in both temporal and spatial domains, have thus inspired many contemporary research studies to revisit established fields. In this paper, we introduce a time-varying metasurface driven radar jamming and deception system (TVM-RJD), which can perfectly overcome the aforementioned intrinsic challenges. Leveraging a programmable bias voltage, the TVM-RJD can alter the spectrum distribution of incident waves, thereby deceiving radar into making erroneous judgments about the target's location. Experimental outcomes affirm that the accuracy deviation of the TVM-RJD system is less than 0.368 meters, while achieving a remarkable frequency conversion efficiency of up to 96.67%. The TVM-RJD heralds the expansion into a wider application of electromagnetic spatiotemporal manipulation, paving the way for advancements in electromagnetic illusion, radar invisibility, etc.

Monotonic Asynchronous Two-Bit Full Adder

Monotonic circuits are a class of input–output mode (IOM) asynchronous circuits that are relaxed compared to quasi-delay-insensitive (QDI) IOM asynchronous circuits in terms of signaling the completion of internal processing. Some recent works have demonstrated the superiority of monotonic logic over QDI logic for arithmetic circuits such as adders and multipliers. This paper presents a new monotonic asynchronous two-bit full adder (TFA) that can be duplicated and cascaded to form a ripple-carry adder (RCA). While an RCA is a slow adder with respect to synchronous design, with respect to IOM asynchronous design an RCA is a noteworthy adder since it has perhaps the least reverse latency that is not attainable through other IOM asynchronous adders. Conventionally, an RCA is constructed via a cascade of one-bit full adders (OFAs). An OFA adds two input bits along with any carry input and produces a sum bit and any carry output. On the other hand, a TFA simultaneously adds two pairs of input bits along with any carry input and produces two sum bits and any carry output. Using our proposed monotonic TFA, we realized an RCA to compare its performance with RCAs constructed using different asynchronous OFAs, and RCAs constructed using existing TFAs. We considered the popular delay-insensitive dual-rail scheme for encoding the adder inputs and outputs, and two 4-phase handshake protocols, namely return-to-zero handshaking (R0H) and return-to-one handshaking (R1H) for communication separately. We used a 28 nm CMOS process for implementation and considered a 32-bit addition as an example. Based on the design metrics estimated, the following inferences were derived: (i) compared to the RCA using the state-of-the-art monotonic OFA, the RCA incorporating the proposed TFA achieved a 26% reduction in cycle time for R0H and a 28.5% reduction in cycle time for R1H while dissipating almost the same power; the cycle time governs the data application rate in an IOM asynchronous circuit, and (ii) compared to the RCA comprising an early output QDI TFA, the RCA incorporating the proposed TFA achieved a 22.3% reduction in cycle time for R0H and a 25.4% reduction in cycle time for R1H while dissipating moderately less power. Also, compared to the existing early output QDI TFA, the proposed TFA occupies 40.9% less area for R0H and 42% less area for R1H.

A Novel Method for Remaining Useful Life Prediction of RF Circuits Based on the Gated Recurrent Unit-Convolutional Neural Network Model.

The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This manuscript proposes a novel RUL prediction method based on a gated recurrent unit-convolutional neural network (GRU-CNN). Firstly, the data are normalized to improve the efficiency of the algorithm; secondly, the degradation of the circuit is evaluated using the hybrid health score based on the Euclidean and Manhattan distances; then, the life cycle of the RF circuits is segmented based on the hybrid health scores; and finally, an RUL prediction is carried out for the circuits at each stage using the GRU-CNN model. The results show that the RMSE of the GRU-CNN model in the normal operation stage is only 3/5 of that of the GRU and CNN models, while the prediction uncertainty is minimized.

Control of polymers’ amorphous-crystalline transition enables miniaturization and multifunctional integration for hydrogel bioelectronics

Soft bioelectronic devices exhibit motion-adaptive properties for neural interfaces to investigate complex neural circuits. Here, we develop a fabrication approach through the control of metamorphic polymers’ amorphous-crystalline transition to miniaturize and integrate multiple components into hydrogel bioelectronics. We attain an about 80% diameter reduction in chemically cross-linked polyvinyl alcohol hydrogel fibers in a fully hydrated state. This strategy allows regulation of hydrogel properties, including refractive index (1.37-1.40 at 480 nm), light transmission (>96%), stretchability (139-169%), bending stiffness (4.6 ± 1.4 N/m), and elastic modulus (2.8-9.3 MPa). To exploit the applications, we apply step-index hydrogel optical probes in the mouse ventral tegmental area, coupled with fiber photometry recordings and social behavioral assays. Additionally, we fabricate carbon nanotubes-PVA hydrogel microelectrodes by incorporating conductive nanomaterials in hydrogel for spontaneous neural activities recording. We enable simultaneous optogenetic stimulation and electrophysiological recordings of light-triggered neural activities in Channelrhodopsin-2 transgenic mice.

Circuit complexity of quantum access models for encoding classical data

How to efficiently encode classical data is a fundamental task in quantum computing. While many existing works treat classical data encoding as a black box in oracle-based quantum algorithms, their explicit constructions are crucial for the efficiency of practical algorithm implementations. Here, we unveil the mystery of the classical data encoding black box and study the Clifford + T complexity in constructing several typical quantum access models. For general matrices (even including sparse ones), we prove that sparse-access input models and block-encoding both require nearly linear circuit complexities relative to the matrix dimension. We also give construction protocols achieving near-optimal gate complexities. On the other hand, the construction becomes efficient with respect to the data qubit when the matrix is a linear combination of polynomial terms of efficiently implementable unitaries. As a typical example, we propose improved block-encoding when these unitaries are Pauli strings. Our protocols are built upon improved quantum state preparation and a select oracle for Pauli strings, which hold independent values. Our access model constructions provide considerable flexibility, allowing for tunable ancillary qubit numbers and offering corresponding space-time trade-offs.