Fundamental Limits Research Articles

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and the architecture of data processing. Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited. Many existing neuromorphic circuits rely on unconventional and useful properties of novel technologies to better mimic the operation of the brain. One such technology is single flux quantum (SFQ) logic—a cryogenic superconductive technology in which the data are represented by quanta of magnetic flux (fluxons) produced and processed by Josephson junctions embedded within inductive loops. The movement of a fluxon within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a neuronal spiking event. These circuits routinely operate at clock frequencies of tens to hundreds of gigahertz, making SFQ a natural technology for processing high frequency pulse trains. This work harnesses thermal stochasticity in superconducting synapses to emulate stochasticity in biological synapses in which the synapse probabilistically propagates or blocks incoming spikes. The authors also present neuronal, fan-in, and fan-out circuitry inspired by the literature that seamlessly cascade with the synapses for deep neural network construction. Synapse weights and neuron biases are set with bias current, and the authors propose multiple mechanisms for training the network and storing weights. The network primitives are successfully demonstrated in simulation in the context of a rate-coded multi-layer XOR neural network which achieves a wide classification margin. The proposed methodology is based solely on existing SFQ technology and does not employ unconventional superconductive devices or semiconductor transistors, making this proposed system an effective approach for scalable cryogenic neuromorphic computing.

An appraisal of the observed crystallinities of volcanic materials

There is a broad consensus that magma eruptibility—the ability of magma stored in the subsurface to erupt onto Earth's surface—is strongly controlled by viscosity. Related to this, a critical parameter that controls viscosity is crystallinity. However, there is uncertainty in the crystallinities that distinguish eruptible from non-eruptible magmas, and whether highly crystalline magmas (>60 vol.%) could be erupted in some conditions. An underutilized but important source of information for understanding this relationship is the observed crystallinities in erupted volcanic materials, which by definition represent a set of eruptible magmas. Here we present a compilation of reported crystallinities for nearly 1000 volcanic samples of differing composition, tectonic setting, and eruption style, which provides valuable insight into the fundamental mechanisms which drive eruptions. Overall, the 95th percentile crystallinity value of our full dataset is 57 vol.%, and \>99 % of all non-dome samples have crystallinity ≤53 vol.%. This suggests that 50–60 vol.% crystallinity represents a fundamental limit for eruptibility for most volcanic rocks. Some dome samples are clear exceptions to this and are erupted with considerably higher crystallinities. There is also a significant correlation between crystallinity and whole rock SiO2 content as observed previously, but a shallow slope suggests whole rock and melt silica content have less impact on critical crystallinity for erupted magma than previously thought. Melt viscosity (as a function of SiO2, temperature, and H2O content) and crystallinity both play important roles on increasing effective viscosity, where melt viscosity plays a more important role at low crystal fractions, and crystallinity plays a more important role at crystallinities greater than ~40 vol.%.

Thermodynamic Correlation Inequality.

Trade-off relations place fundamental limits on the operations that physical systems can perform. This Letter presents a trade-off relation that bounds the correlation function, which measures the relationship between a system's current and future states, in Markov processes. The obtained bound, referred to as the thermodynamic correlation inequality, states that the change in the correlation function has an upper bound comprising the dynamical activity, a thermodynamic measure of the activity of a Markov process. Moreover, by applying the obtained relation to the linear response function, it is demonstrated that the effect of perturbation can be bounded from above by the dynamical activity.

On the General Current Dependence of the Distributed Series Resistance of Solar Cells: The Influence of the Base Resistivity

The lumped series resistance Rs of a silicon solar cell isn’t constant but depends on the operating point of the solar cell. For describing the relevant current dependence analytically, only few theories exist that can easily be applied to experiments. These are: (i) the ad-hoc modelling by Araújo et al., modified by Breitenstein et al., and (ii) the LR-Rs approach by Wagner et al. Both have fundamental limitations: Whereas Araújo’s ad-hoc model is partly unphysical, the LR-Rs approach misses the influence of the base resistivity. Here, we discuss these shortcomings, and we show where to include the base resistivity in the LR-Rs approach. In addition, we discuss the current dependence of the lumped series resistance of a solar cell in general terms, thereby clarifying the underlying physical basics.

Highly-sensitive wafer-scale transfer-free graphene MEMS condenser microphones

Since the performance of micro-electro-mechanical system (MEMS)-based microphones is approaching fundamental physical, design, and material limits, it has become challenging to improve them. Several works have demonstrated graphene’s suitability as a microphone diaphragm. The potential for achieving smaller, more sensitive, and scalable on-chip MEMS microphones is yet to be determined. To address large graphene sizes, graphene-polymer heterostructures have been proposed, but they compromise performance due to added polymer mass and stiffness. This work demonstrates the first wafer-scale integrated MEMS condenser microphones with diameters of 2R = 220–320 μm, thickness of 7 nm multi-layer graphene, that is suspended over a back-plate with a residual gap of 5 μm. The microphones are manufactured with MEMS compatible wafer-scale technologies without any transfer steps or polymer layers that are more prone to contaminate and wrinkle the graphene. Different designs, all electrically integrated are fabricated and characterized allowing us to study the effects of the introduction of a back-plate for capacitive read-out. The devices show high mechanical compliances Cm = 0.081–1.07 μmPa−1 (10–100 × higher than the silicon reported in the state-of-the-art diaphragms) and pull-in voltages in the range of 2–9.5 V. In addition, to validate the proof of concept, we have electrically characterized the graphene microphone when subjected to sound actuation. An estimated sensitivity of S1kHz = 24.3–321 mV Pa−1 for a Vbias = 1.5 V was determined, which is 1.9–25.5 × higher than of state-of-the-art microphone devices while having a ~9 × smaller area.

Exploring the fundamental limits of integrated beam splitters with arbitrary phase via topology optimization.

Optical beam splitters are essential for classical and quantum photonic on-chip systems. In integrated optical technology, a beam splitter can be implemented as a beam coupler with two input and two output ports. The output phases are constrained by the conservation of energy. In lossless beam splitters, the phase shift between the output fields is π and zero for excitation from the first and second input ports, respectively. Therefore, for excitation from both inputs, the phase between the output fields, defined as beam splitter phase (BSP), is π. The BSP leads to several phenomena, such as the quantum interference between two photons, known as the Hong-Ou-Mandel effect. By introducing losses, BSP values different than π become theoretically possible, but the design of 2 × 2 beam couplers with an arbitrary phase is elusive in integrated optics. Inspired by the growing interest on fundamental limits in electromagnetics and inverse design, here we explore the theoretical limits of symmetrical integrated beam splitters with an arbitrary BSP via adjoint-based topology optimization. Optimized 2D designs accounting for fabrication constraints are obtained for several combinations of loss and phase within the theoretical design space. Interestingly, the algorithm does not converge for objectives outside of the theoretical limits. Designs of beam splitters with arbitrary phase may find use in integrated optics for quantum information processing.

Measuring Protein-Ligand Binding by Hyperpolarized Ultrafast NMR.

Protein-ligand interactions can be detected by observing changes in the transverse relaxation rates of the ligand upon binding. The ultrafast NMR technique, which correlates the chemical shift with the transverse relaxation rate, allows for the simultaneous acquisition of R2 for carbon spins at different positions. In combination with dissolution dynamic nuclear polarization (D-DNP), where the signal intensity is enhanced by thousands of times, the R2 values of several carbon signals from unlabeled benzylamine are observable within a single scan. The hyperpolarized ultrafast chemical shift-R2 correlated experiment separates chemical shift encoding from the readout phase in the NMR pulse sequence, which allows it to beat the fundamental limit on the spectral resolution otherwise imposed by the sampling theorem. Applications enabled by the ability to measure multiple relaxation rates in a single scan include the study of structural properties of protein-ligand interactions.

Synthetic CT imaging for PET monitoring in proton therapy: a simulation study.

Objective.This study addresses a fundamental limitation of in-beam positron emission tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data.Approach.We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a visual transformer (ViT) neural network, we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model's performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images-serving as the benchmark.Main results.The structural similarity index was computed at a mean value across all patients of 0.91, while the mean absolute error measured 22 Hounsfield Units (HU). Root mean squared error and peak signal-to-noise ratio values were 56 HU and 30 dB, respectively. The Dice similarity coefficient exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT scan.Significance.Our study presents an innovative approach to surface the hidden anatomical information of IB-PET in proton therapy. Our ViT-based model successfully generates sCT images from inter-fractional PET data and planning CT scans. Our model's performance stands on par with existing models relying on input from cone beam CT or magnetic resonance imaging, which contain more anatomical information than activity maps.

PRECLINICAL ASSESSMENT OF NITROGEN-CONTAINING HETEROCYCLIC DERIVATIVES FOR BREAST CANCER INTERVENTION IN ANIMAL MODELS

Due to their inherent adaptability and distinctive physicochemical characteristics, the majority of heterocycle compounds and generally frequent heterocycle units found in the majority of medications now on the market have established them as genuine bases of medicinal chemistry. In addition to the medications that are now on the market, several more are being researched for their possible effectiveness in treating a variety of cancer. Specifically, these molecules inherent adaptability and dynamic core scaffold have been used in anticancer research. However, much like every other potential anticancer medication, heterocyclic compounds have drawbacks. We give a succinct summary of heterocyclic active compounds and families in this study, along with an emphasis on their primary medical uses.While examining the primary biochemical mechanisms of action, biological targets, structure-activity connections, and fundamental limitations in the utilisation of these substances, we have concentrate on those that are appropriate for cancer therapy.

Fundamental limits on quantum cloning from the no-signaling principle

Fundamental limits on quantum cloning from the no-signaling principle

Design of a Stacked Dual-Patch Antenna with 3D Printed Thick Quasi-Air Substrates and a Cavity Wall for Wideband Applications

In this paper, we propose a stacked dual-patch antenna with 3D printed thick quasi-air substrates and a cavity wall for wideband applications. To achieve the theoretical maximum bandwidth of the patch antenna, the quality factor of the system needs to be minimized. To achieve this, the area of the conductive radiator should be enlarged, while the permittivity of the substrate within the patch must be reduced close to 1. To realize a patch antenna with this maximum bandwidth, the stacked dual-patch configuration is employed to obtain an extended conductive radiator area. In addition, square-pipe resin frames manufactured using a 3D printing method are applied to the proposed antenna to implement a quasi-air substrate structure that has a low permittivity value close to 1. The proposed stacked dual-patch antenna with a quasi-air substrate has a broad bandwidth of 20.7%. The results demonstrate that by using the proposed antenna structure, broadband characteristics close to the fundamental bandwidth limit of the patch antenna can be achieved.

Adaptive nonequilibrium design of actin-based metamaterials: Fundamental and practical limits of control

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding "memory" within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of "encoding" that gives insight into the constraints on control-both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.

Exploring FPGA Switch-Blocks without Explicitly Listing Connectivity Patterns

Increased lower metal resistance makes physical aspects of Field-Programmable Gate Array (FPGA) switch-blocks more relevant than before. The need to navigate a design space where each individual switch can have significant impact on the FPGA’s performance in turn makes automated switch-pattern exploration techniques increasingly appealing. However, most existing exploration techniques have a fundamental limitation—they use the CAD tools as a black box to evaluate the performance of explicitly listed switch-patterns. Given the time needed to route a modern circuit on a single architecture, the number of switch-patterns that can be explicitly tested quickly becomes negligible compared to the size of the design space. This article presents a technique that removes this fundamental limitation by making the entire design space visible to the router and letting it choose the switches to be added to the pattern, based on the requirements of the circuits being routed. The key to preventing the router from selecting arbitrary switches that would render the final pattern excessively large is to apply the same negotiation principle used by the router to remove congestion, just in the opposite direction, to make the signals reach a consensus on which switches are worthy of being included in the final switch-pattern.

The decimation scheme for symmetric matrix factorization

Matrix factorization is an inference problem that has acquired importance due to its vast range of applications that go from dictionary learning to recommendation systems and machine learning with deep networks. The study of its fundamental statistical limits represents a true challenge, and despite a decade-long history of efforts in the community, there is still no closed formula able to describe its optimal performances in the case where the rank of the matrix scales linearly with its size. In the present paper, we study this extensive rank problem, extending the alternative ‘decimation’ procedure that was recently introduced by Camilli and Mézard, and carry out a thorough study of its performance. Decimation aims at recovering one column/line of the factors at a time, by mapping the problem into a sequence of neural network models of associative memory at a tunable temperature. The main advantage of decimation is that its theoretical performance can be studied using statistical physics techniques. In this paper we provide the general analysis applying to a large class of compactly supported priors and we show that the replica symmetric free entropy of the neural network models takes a universal form in the low temperature limit. We then study the decimation phase diagrams in two concrete cases, the sparse Ising prior and the uniform prior, for which we show that matrix factorization is theoretically possible when the ration P/N of rank to variable is below a certain threshold. This suggest that a possible route to solving algorithmically the matrix factorization problem could be to find efficient decimation algorithms; in this respect, we show that simple simulated annealing, though being effective for limited signal sizes, is not scalable.

New Tuning Rules of PI+CI Controllers for First-Order Systems

The reset control is a simple nonlinear control approach where the states of the controller are conducted to zero when a particular condition is satisfied. The PI+CI is a controller that mixes the simplicity of PI controllers with the benefits of a reset action to mitigate the fundamental limitations of linear control. However, the tuning of this kind of controller, with three parameters, two for the linear part and one for the nonlinear one, is not trivial. In this paper, simple tuning rules for PI+CI are proposed for both tracking and regulation problems, assuming first-order dynamics for the plant. The resulting control scheme, for which the reset coefficient is computed from exponential functions, is simulated and compared with an ideal PI+CI where the reset coefficient is obtained using rules available in the literature. Similar results are obtained for the tracking problem, and optimal performance based on the Integral Absolute Error (IAE) is also obtained for the regulation problem. These new rules, in contrast to those already existing in the literature, depend only on closed-loop specifications. Furthermore, the framework based on the minimization of IAE, used to obtain the proposed rules, makes it possible to consider for the first time the tracking and regulation problems simultaneously, i.e., cases where setpoint changes and disturbance arrivals can occur at the same time before reaching a new steady state. The results are validated using a set of study cases.

Seeing in crowds: Averaging first, then max.

Crowding, a fundamental limit in object recognition, is believed to result from excessive integration of nearby items in peripheral vision. To understand its pooling mechanisms, we measured subjects' internal response distributions in an orientation crowding task. Contrary to the prediction of an averaging model, we observed a pattern suggesting that the perceptual judgement is made based on choosing the largest response across the noise-perturbed items. A model featuring first-stage averaging and second-stage signed-max operation predicts the diverse errors made by human observers under various signal strength levels. These findings suggest that different rules operate to resolve the bottleneck at early and high-level stages of visual processing, implementing a combination of linear and nonlinear pooling strategies.

Current status and challenges for hole-selective poly-silicon based passivating contacts

Doped polysilicon (poly-Si) passivating contacts have emerged as a key technology for the next generation of silicon solar cells in mass production, owing to their excellent performance and high compatibility with the existing passivated emitter and rear cell technology. However, the current solar cell architecture based on a rear-side electron-selective (n+) poly-Si contact is also approaching its practical limit (∼26%) in mass production. The full potential of doped poly-Si passivating contacts can only be realized through incorporation of both electron-selective and hole-selective (p+) poly-Si contacts. While studies of both p+ and n+ poly-Si contacts commenced simultaneously, significant performance differences have arisen. Phosphorus-doped poly-Si contacts consistently outperform boron-doped counterparts, displaying typically lower recombination current density (J0) values (1–5fA/cm2 vs 7–15fA/cm2). This discrepancy can be attributed to inadequate optimization of p+ poly-Si contacts and fundamental limitations related to boron doping. The poorer passivation of p+ poly-Si contacts can be at least partly attributed to boron segregation into the interfacial oxide layers, compromising the interfacial oxide integrity and reducing the chemical passivation effectiveness. This review critically examines the progress of p+ poly-Si contacts characterized by cell efficiency and J0 values, delves into existing challenges, identifies potential solutions, and explores some potential solar cell architectures to enhance efficiency by incorporating p+ poly-Si contacts.

Neural correlates of cognitive ability and visuo-motor speed: Validation of IDoCT on UK Biobank Data

Abstract Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability, and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuo-motor speeds, testing device latencies, and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuo-motor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuo-motor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuo-motor versus language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities, and brain-wide associations.

Strategies for optimal sky subtraction in the low surface brightness regime

ABSTRACT The low surface brightness (LSB) regime (μg ≳ 26 mag arcsec−2) comprises a vast, mostly unexplored discovery space, from dwarf galaxies to the diffuse interstellar medium. Accessing this regime requires precisely removing instrumental signatures and light contamination, including, most critically, night sky emission. This is not trivial, as faint astrophysical and instrumental contamination can bias sky models at the precision needed to characterize LSB structures. Using idealized synthetic images, we assess how this bias impacts two common LSB-oriented sky-estimation algorithms: (1) masking and parametric modelling, and (2) stacking and smoothing dithered exposures. Undetected flux limits both methods by imposing a pedestal offset to all derived sky models. Careful, deep masking of fixed sources can mitigate this, but source density always imposes a fundamental limit. Stellar scattered light can contribute ∼28–29 mag arcsec−2 of background flux even in low-density fields; its removal is critical prior to sky estimation. For complex skies, image combining is an effective non-parametric approach, although it strongly depends on observing strategy and adds noise to images on the smoothing kernel scale. Preemptive subtraction of fixed sources may be the only practical approach for robust sky estimation. We thus tested a third algorithm, subtracting a preliminary sky-subtracted coadd from exposures to isolate sky emission. Unfortunately, initial errors in sky estimation propagate through all subsequent sky models, making the method impractical. For large-scale surveys like Legacy Survey of Space and Time, where key science goals constrain observing strategy, masking and modelling remain the optimal sky estimation approach, assuming stellar scattered light is removed first.

High-permittivity ceramics enabled highly homogeneous zero-index metamaterials for high-directivity antennas and beyond

Zero-index metamaterials (ZIMs) can support uniform electromagnetic field distributions at any frequency, but their applications are hampered by the ZIM’s homogenization level—only 3 unit cells per free-space wavelength, which is fundamentally limited by the low-permittivity inclusions (εr ≈ 12) and background matrix (εr ≈ 1). Here, by filling high-permittivity SrTiO3 ceramic (εr ≈ 294) pillars in BaTiO3 (εr ≈ 25) background matrix, we demonstrate a highly homogeneous microwave ZIM with an over threefold increase in the homogenization level. Leveraging such a ZIM, we achieve not only an antenna, approaching the fundamental limit in the directivity with outstanding scalability, but also a concave lens with a focal length of as short as 1λ0. Our highly homogeneous ZIM has profound implications in ceramics, ZIM-based waveguides and cavities, free-space wavefront manipulation, and microwave quantum optics, and opens up enormous possibilities in wireless communications, remote sensing, global positioning satellites, etc.