Reconfigurable Function Research Articles

A high-reliability SEPIC converter with reconfigurable voltage conversion gain

A high-reliability SEPIC converter with reconfigurable voltage conversion gain is proposed, which is composed of a basic SEPIC converter and n extended units. Adopting an interleaved parallel structure on the input side of the converter not only achieves leg-level hardware redundancy but also reduces the input current ripple. When one branch of the converter suddenly changes or fails, due to the redundancy between branches, the converter can still to work, and the duty cycle of the other branches can be readjusted by using PI control without changing output voltage and power of the converter. This feature undoubtedly presents the reliability of the converter. On the contrary, when the converter works in normal operation, it can also actively control operation mode of each branch to achieve reconfigurable voltage conversion gain function. In addition, since the duty cycles of the switches are not limited, voltage conversion gain of the converter is widely, which makes it more suitable for the occasions where the input side fluctuates greatly. Section 2 details the working principle of the proposed converter and the voltage and current stress on components. Then, the reconfigurable voltage conversion gain of the converter and its high reliability characteristics are analyzed in Section 3. Finally, the correctness of the theoretical analysis is verified by experiments.

A complementary low-Schottky-barrier S/D-based nanoscale dopingless bidirectional reconfigurable field effect transistor with an improved forward current

In this paper, a nanoscale dopingless bidirectional RFET (BRFET) is proposed. Unlike conventional BRFETs, the proposed BRFET uses two different metal materials to form two different types of Schottky barriers on the interface between the S/D and silicon. For one of the two metal forms, the Schottky barrier height between the conduction band of the semiconductor and one of the two metal materials is lower than half of the energy band gap. The Schottky barrier height between the valence band of the semiconductor and the other kind of the two metal materials is lower than half of the energy band gap of the semiconductor. Therefore, a complementary low Schottky barrier (CLSB) is formed. Therefore, more carriers from the source electrode can easily flow into the semiconductor region through thermionic emission in both n-mode and p-mode compared to conventional BRFET operation, which generates carriers through the band-to-band tunneling effect. Therefore, a larger forward current can be achieved by the proposed CLSB-BRFET. The performance of the CLSB-BRFET is investigated by device simulation and compared with that of the BRFET. The working principle is interpreted through an analysis based on energy band theory. The output characteristics and reconfigurable function are also investigated and verified.

Foreign speech sound discrimination and associative word learning lead to a fast reconfiguration of resting-state networks

Learning new words in an unfamiliar language is a complex endeavor that requires the orchestration of multiple perceptual and cognitive functions. Although the neural mechanisms governing word learning are becoming better understood, little is known about the predictive value of resting-state (RS) metrics for foreign word discrimination and word learning attainment. In addition, it is still unknown which of the multistep processes involved in word learning have the potential to rapidly reconfigure RS networks. To address these research questions, we used electroencephalography (EEG), measured forty participants, and examined scalp-based power spectra, source-based spectral density maps and functional connectivity metrics before (RS1), in between (RS2) and after (RS3) a series of tasks which are known to facilitate the acquisition of new words in a foreign language, namely word discrimination, word-referent mapping and semantic generalization. Power spectra at the scalp level consistently revealed a reconfiguration of RS networks as a function of foreign word discrimination (RS1 vs. RS2) and word learning (RS1 vs. RS3) tasks in the delta, lower and upper alpha, and upper beta frequency ranges. Otherwise, functional reconfigurations at the source level were restricted to the theta (spectral density maps) and to the lower and upper alpha frequency bands (spectral density maps and functional connectivity). Notably, scalp RS changes related to the word discrimination tasks (difference between RS2 and RS1) correlated with word discrimination abilities (upper alpha band) and semantic generalization performance (theta and upper alpha bands), whereas functional changes related to the word learning tasks (difference between RS3 and RS1) correlated with word discrimination scores (lower alpha band). Taken together, these results highlight that foreign speech sound discrimination and word learning have the potential to rapidly reconfigure RS networks at multiple functional scales.

Topologically Triggered Dynamic DNA Frameworks

AbstractTopologically triggered dynamic nucleic acid circuitries play important roles in diverse biological processes. Biomimetic frameworks emulating topologically triggered native circuitries is a challenging topic in the area of Systems Chemistry. Dynamically reconfigurable functions encoded in synthetically engineered nucleic acids provide means to design topologically‐triggered dynamic DNA frameworks. This topic is addressed with three examples: (i) Integration of reconfigurable G‐quadruplex barriers into polymerization or transcription templates leading to the switchable synthesis of a DNAzyme or of an RNA aptamer as models for native gene expression instability processes. (ii) Design of dynamic transient transcription and gated transcription machineries using an autoinhibition reaction module. (iii) Design of dynamic DNA reaction moduli leading to transient biocatalytic cascades, transient aggregation/deaggregation of semiconductor quantum dots or supramolecular Ag nanoclusters.

Self-folding soft-robotic chains with reconfigurable shapes and functionalities

Magnetic continuum soft robots can actively steer their tip under an external magnetic field, enabling them to effectively navigate in complex in vivo environments and perform minimally invasive interventions. However, the geometries and functionalities of these robotic tools are limited by the inner diameter of the supporting catheter as well as the natural orifices and access ports of the human body. Here, we present a class of magnetic soft-robotic chains (MaSoChains) that can self-fold into large assemblies with stable configurations using a combination of elastic and magnetic energies. By pushing and pulling the MaSoChain relative to its catheter sheath, repeated assembly and disassembly with programmable shapes and functions are achieved. MaSoChains are compatible with state-of-the-art magnetic navigation technologies and provide many desirable features and functions that are difficult to realize through existing surgical tools. This strategy can be further customized and implemented for a wide spectrum of tools for minimally invasive interventions.

All-Optical Recurrent Neural Network With Reconfigurable Activation Function

Optical Neural Networks (ONNs) can be promising alternatives for conventional electrical neural networks as they offer ultra-fast data processing with low energy consumption. However, lack of suitable nonlinearity is standing in their way of achieving this goal. While this problem can be circumvented in feed-forward neural networks, the performance of the recurrent neural networks (RNNs) depends heavily on their nonlinearity. In this paper, we first propose and numerically demonstrate a novel reconfigurable optical activation function, named ROA, based on adding or subtracting the outputs of two saturable absorbers (SAs). RAO can provide both bounded and unbounded outputs by facilitating an electrically programmable adder/subtractor design. Second, with the help of RAO, which can be altered to resemble Tanh or Sigmoid activation functions, we take a step further and numerically demonstrate OptoRNN, a new design for an all-optical RNN in free-space optics. Moreover, by benefiting from mathematical modeling of the multiplication noise, as well as an altered loss function, we enable vector-matrix multiplication (VMM) six times more parallel than conventional optical VMM method for the linear part of the OptoRNN. Finally, through comprehensive simulation studies, we demonstrate that utilizing the OptoRNN, we can achieve 96.3% train accuracy for sequential-MNIST dataset.

System Identification–Based Adaptive Real-Time Simulators for Power Electronic Converters—Application to Three-Phase and NPC Inverters

This paper deals with the real-time simulation of power electronic converters. It discusses a new approach for designing embedded real-time simulators (eRTSs) that approximate the static and dynamic behavior of a power converter at the switching scale. The main concept is to approximate the voltage/current experimental characteristics of each switch using dedicated transfer functions obtained after a system identification process. The adaptive feature of such eRTS consists of developing varying and online reconfigurable coefficients transfer functions. The main potential of doing so is the possibility of reconfiguring the model according to the actual electrical/thermal environment where the power converter is used. Then, the latter is subdivided into independent switching cells, represented by dedicated RT models that are fully parallelized. Furthermore, using FPGA devices makes it possible to achieve very low latencies and, consequently, a short simulation time step. Previous work was published in this context, where this approach was deeply described and tested with half-bridge DC–DC, full-bridge DC–AC, and multi-level cascaded H-bridge (five-level and nine-level) power converters. This paper recalls the main basics and, more importantly, discusses additional case studies, namely a three-phase voltage source inverter, a half-bridge NPC (neutral-point clamped) inverter, and a three-phase NPC inverter.

GeTe2 Phase Change Material for Terahertz Devices with Reconfigurable Functionalities Using Optical Activation.

The phenomenon of phase change transition has been a fascinating research subject over decades due to a possibility of dynamically controlled materials properties, allowing the creation of optical devices with unique features. The present paper unravels the optical characteristics and terahertz (THz) dielectric permittivity of a novel phase change material (PCM), GeTe2, prepared by pulsed laser deposition (PLD) and their remarkable contrast in crystalline and amorphous states, in particular, a difference of 7 orders of magnitude in conductivity. The THz spectra were analyzed using the harmonic oscillator and Drude term. Using GeTe2 PLD films, we designed and prepared a THz metasurface in the form of periodic structure and revealed a possibility of tuning the THz resonance either by a thermal control or light-induced crystallization response, thus achieving the dynamic and tunable functionality of the metastructure. We propose controlling the state of metasurface by observing the intensity characteristics of the Raman peak of 155 cm-1. Density functional theory (DFT) modeling demonstrates that in the process of crystallization the mode intensity of 155 cm-1 assigned to Te-Te stretching in amorphous chain fragments decreases and disappears at full crystallization.

A Security-Enhanced, Charge-Pump-Free, ISO14443-A-/ISO10373-6-Compliant RFID Tag With 16.2-μW Embedded RRAM and Reconfigurable Strong PUF

Radio frequency identification technology (RFID) has empowered a wide variety of automation industries, such as logistics and freight transportation. To further promote RFID tags adoption, security, power consumption, and cost have always been issues of general concern. This article presents the first synergy of the RFID tag with embedded resistive RAM (RRAM) array and RRAM-based reconfigurable strong physical unclonable function (R-SPUF). The RRAM not only meets the mass storage and technology downscaling but also renders the ultralow-cost “1-cent RFID tag” more feasible. Moreover, the R-SPUF facilitates multiple initializations until a satisfactory distribution and has strong secure keys benefiting from its reconfigurability that improves both safety and reliability. The complete system operates at 13.56 MHz and is compliant with the ISO14443-A and ISO10373-6 (test) protocols. The RFID tag was fabricated on a 1.1-mm2 die based on the 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. Without resorting to the charge pumps for RRAM read–write operations, the total power consumption is as low as 52.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> , of which the RRAM dissipates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.2~\mu \text{W}$ </tex-math></inline-formula> under a wireless power supply.

Reconfigurable microwave photonic mixer for hybrid macro-micro cellular systems.

Microwave photonic mixing can realize the frequency conversion of microwave signals in the optical domain, breaking through the bandwidth bottleneck and electromagnetic interference problems of traditional microwave mixing methods. In the background of the hybrid macro-micro cellular system, a wideband, large dynamic range and reconfigurable microwave photonic mixer is proposed, theoretically analyzed and experimentally demonstrated in this paper. By adjusting the modulator bias voltages and matching the proper digital domain operations, a microwave photonic mixer with reconfigurable functions including single-ended dispersion immune mixing, I/Q frequency down-conversion, image rejection mixing, and double-balanced mixing are realized, respectively. Meanwhile, optimizing the electrical attenuator using convex optimization can suppress the third-order intermodulation distortion (IMD3), maximize the conversion gain, and finally improve the spur-free dynamic range (SFDR). Experimental results show that the proposed scheme can be operated with a frequency from 5 to 20 GHz, and the SFDR can achieve 118.3 dB·Hz4/5. Over the whole frequency range, I/Q frequency down-conversion can be well conducted with an amplitude imbalance below 0.7 dB and a phase imbalance below ±0.7°. After an I/Q imbalance compensation algorithm, the image rejection ratio of over 60 dB is produced. The power fading caused by fiber dispersion is also compensated successfully. For a vector signal with 16 quadrature amplitude modulation, the best error vector magnitude (EVM) reaches 3.4%.

Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology

A novel multi-beam photonic receiver with a variable beam count has been proposed. The reconfigurable characteristic of the system is enabled with the variable splitters and optical switch arrays. According to the number of beams it is due to receive, the splitter with suitable ports is chosen to split by controlling the optical switch in order to configure the optical path. After splitting, the multi-wavelength optical carriers are sent to the wavelength division multiplexer to combine and complete the multi-beamforming process. Two structures have been designed to realize the reconfigurable function, while the optimized structure is proved by using several benchmarks for comparison. The operation step of the reconfigurable multi-beam receiver is then given. The first simulations validate that the proposed architecture can dynamically receive the multi-beam with a variable number by reconfiguring the state of the optical switch. The second simulation of the influence on the received beam pattern was performed under a different amplitude and phase inconsistency random values. The results show that the amplitude and phase inconsistency will decline the quality of the multi-beam. When the amplitude and phase inconsistency are within limits, the difference in the optical path within the outputs must be balanced and compensated during the proposed system’s fabrication and deployment.

Electromagnetic Reconfiguration Using Stretchable Mechanical Metamaterials.

Response to environmental thermomechanical inputs in applications that range from wearable electronics to aerospace structures necessitates agile communication systems driven by reconfigurable electromagnetic structures. Antennas in these systems must dynamically preserve acceptable radiation characteristics while enabling on-demand performance reconfiguration. However, existing reconfiguration mechanisms through stretchable conductors rely on high-strain behavior in soft substrates, which limits their applicability. Herein, this work demonstrates the use of mechanical metamaterials for stretchable conductors and dielectrics in antennas. Metamaterials allow conductor stretching up to 30% with substrate base material tensile moduli ranging from 26MPa to 44GPa. It is shown, through several antenna designs, that mechanical metamaterials enable similar frequency reduction upon stretching as monolithic conductors, while simultaneously providing a miniaturization effect. The conductor patterning, furthermore, provides control over coupling between mechanical stretching and electromagnetic reconfiguration. This approach enables designing reconfigurable antenna functionality through metamaterial geometry in response to arising needs in applications ranging from body-adapted electronics to space vehicles.

Ultrahigh dynamic range and low noise figure programmable integrated microwave photonic filter

Microwave photonics has adopted a number of important concepts and technologies over the recent pasts, including photonic integration, versatile programmability, and techniques for enhancing key radio frequency performance metrics such as the noise figure and the dynamic range. However, to date, these aspects have not been achieved simultaneously in a single circuit. Here, we report a multi-functional photonic integrated circuit that enables programmable filtering functions with record-high performance. We demonstrate reconfigurable filter functions with record-low noise figure and a RF notch filter with ultra-high dynamic range. We achieve this unique feature using versatile complex spectrum tailoring enabled by an all integrated modulation transformer and a double injection ring resonator as a multi-function optical filtering component. Our work breaks the conventional and fragmented approach of integration, functionality and performance that currently prevents the adoption of integrated MWP systems in real applications.

Nonlinear Oscillation States of Optomechanical Resonator for Reconfigurable Light‐Compatible Logic Functions

AbstractAn optomechanical scheme is reported to implement single devices combining an electromagnetic resonator with a strongly nonlinear nanomechanical oscillator in order to realize light‐controlled logic functions. In this scheme, the nonlinear mechanical oscillations, which can be well controlled by external drives, are revealed to be sensitive to incident light due to light–matter interactions enhanced by the resonator. As a result, one can adjust the initial mechanical configurations to obtain various on‐demand responses to input optical signals, and thus realize reconfigurable logic functions (NOT, XOR, OR, AND). The single optomechanical device reported here is designed to work at the terahertz (THz) frequency domain, which is the first demonstration of a THz‐compatible logic component that processes information encoded in modulated free‐space THz light, with operation rates reaching a few kilohertz. This device also can be integrated into other micro‐electromechanical systems as THz‐compatible actuators. Moreover, the optomechanical scheme proposed in this work is applicable to other frequency ranges, such as radio frequencies (RF) or the infrared.

Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.

Reconfigurable Low-Threshold All-Optical Nonlinear Activation Functions Based on an Add-Drop Silicon Microring Resonator

The realization of optical nonlinear activation functions (NAFs) is essential for integrated optical neural networks (ONNs). Here, we propose and experimentally demonstrate a photonic method to implement reconfigurable and low-threshold all-optical NAFs based on a compact and high-Q add-drop microring resonator (MRR) on silicon. In the experiment, four different NAFs including softplus, radial basis, clamped ReLU, and sigmoid functions are realized by exploiting the thermo-optical (TO) effect of the MRR. The threshold to implement NAFs is as low as 0.08 mW. As a demonstration, a handwritten digit classification benchmark task is simulated based on a convolutional neural network (CNN) using the obtained activation functions, where an accuracy of 98% is realized. Thanks to the unique advantages of ultra-compact footprint and ultralow threshold, the proposed nonlinear unit is promising to be widely used in large-scale integrated ONNs.

Rubik's Cube as Reconfigurable Microfluidic Platform for Rapid Setup and Switching of Analytical Devices.

Microfluidics technology plays an important role in modern analytical instruments, while the modular design of microfluidics facilitates the reconfiguration of analytical instrument functions, making it possible to deploy on-demand systems in the field. However, modular design also faces the challenges such as connection reliability and reconfiguration convenience. Inspired by the self-locking structure of the Rubik's cube, a modular, reconfigurable microfluidic instrument architecture is proposed in this paper. The system has a self-locking structure of Rubik's cube components and an O-ring-based alignment and sealing mechanism, which enables reliable interconnection and rapid rearrangement of microfluidic modules by simply rotating the faces of the microfluidic cube. In addition, the system is capable of integrating a variety of customized modules to perform analysis tasks. A proof-of-concept application of detecting multiple pollutants in water is demonstrated to show the reconfigurable characteristics of the system. The findings of this paper provide a new idea for the design of microfluidic analytical instrument architectures.

Functional reconfiguration of task-active frontoparietal control network facilitates abstract reasoning.

While the brain's functional network architecture is largely conserved between resting and task states, small but significant changes in functional connectivity support complex cognition. In this study, we used a modified Raven's Progressive Matrices Task to examine symbolic and perceptual reasoning in human participants undergoing fMRI scanning. Previously, studies have focused predominantly on discrete symbolic versions of matrix reasoning, even though the first few trials of the Raven's Advanced Progressive Matrices task consist of continuous perceptual stimuli. Our analysis examined the activation patterns and functional reconfiguration of brain networks associated with resting state and both symbolic and perceptual reasoning. We found that frontoparietal networks, including the cognitive control and dorsal attention networks, were significantly activated during abstract reasoning. We determined that these same task-active regions exhibited flexibly-reconfigured functional connectivity when transitioning from resting state to the abstract reasoning task. Conversely, we showed that a stable network core of regions in default and somatomotor networks was maintained across both resting and task states. We propose that these regionally-specific changes in the functional connectivity of frontoparietal networks puts the brain in a "task-ready" state, facilitating efficient task-based activation.

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch

Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach–Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS2 Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).

Mass Diffusion Metamaterials with “Plug and Switch” Modules for Ion Cloaking, Concentrating, and Selection: Design and Experiments (Adv. Sci. 30/2022)

Modular Mass Diffusion Metamaterials A metamaterial design with reconfigurable functions realized by pluggable functional modules is proposed. In article number 2201032, Yang Bai and co-workers demonstrate that ion cloak, ion concentrator, and ion selector can effectively manipulate the diffusion behaviors of ions in liquids solvents, which are expected to be widely used in applications such as catalytic enhancement and protein protection.