Low-power Devices Research Articles

Chiral Molecular Magnet Superstructures with Light Control.

Chiral magnets are crucial for magneto-optical coupling to advance spin-optoelectronics. Chirality breaks spatial inversion symmetry, while magnetism breaks time-reversal symmetry. However, understanding and controlling the interplay between chirality and magnetism remain fundamental challenges. Here we report chiral helical magnetic superstructures with spin tunability and the Faraday effect by circularly polarized photons. By controlling the supramolecular assembly of chiral molecules, we demonstrate the superstructure transition of molecular magnets from vortex to helical nanowire structures through circular dichroism and electron microscopy. The chiral magnets exhibit circularly polarized light controlled ferromagnetic magnetic resonance and magnetic anisotropy. The enhancement of the Faraday effect by chiral structures is comparable to the effect produced by a 3 kOe magnetic field. This approach shows potential for low-power magneto-optical devices, and additionally, it lays the groundwork for chiral light-related noncontact optical magnetics.

Enhancing the energy harvesting of micro-notched turbine for applications in low power devices

It is the future trend to energize small home appliances, medical devices, micro-devices, etc. through miniature generation. Micro-Notched turbine (MiNT) converts mechanical energy to electrical energy for portal devices. The current challenge is to harness the maximum energy by modifying its design, model, blade size, and casing. The MiNT presented in this research is one of the few specialized miniature harvesters with desirable characteristics and the potential to be optimized for maximum power generation. To reap maximum benefit from MiNT, the design of the turbine, the shape of the blades, and the tracking system were modified. Similarly, the size of the MiNT turbine was decreased, and its efficiency was enhanced. In this research, the particle swarm optimization algorithm and resistor emulation technique were employed to enhance Maximum Power Point Tracking (MPPT). Meanwhile, the converter efficiency was also observed. For the validation of results, the proposed technique was compared with passive MPPT and adaptive MPPT techniques. During the simulation, micro-notched-based energy collection devices with upgraded MPPT technology were used. By implementing this hybrid system, the maximum power point was accurately tracked, resulting in improved power output, reduced system size, and improved converter efficiency.

Real-Time Multiple Object Detection Using Raspberry Pi and Tiny-ML Approach

Introduction:: Object detection has been an essential task in computer vision for decades, and modern developments in computer vision and deep learning have greatly increased the accuracy of detecting systems. However, the high computational requirements of deep learningbased object detection algorithms limit their applicability to resource-constrained systems, such as embedded devices. Method:: With the advent of Tiny Machine Learning (TinyML) devices, such as Raspberry Pi, it has become possible to deploy object detection systems on small, low-power devices. Due to their accessibility and cost, Tiny-ML devices, such as Raspberry Pi, a single-board tiny-ML device that is extremely well-liked, have recently attracted a lot of attention. Result:: In this study, we present an enhanced SSD-based object detection approach and deploy the model using a tinyML device, i.e., Raspberry Pi. Conclusion:: The proposed object detection model is lightweight and built utilizing Mobilenet- V2 as the underlying foundation.

Design of Piezoelectric Energy Harvester Single Cantilever Beam for IoT applications and Micro-electronic devices

A few years back the power requirement of electronic devices was very high. But with the technological developments in the field of internet-based systems, the design of low-powered microelectronic devices, WSN and IoT devices became necessary. In these systems the size and the power requirement are low and in most situations the replacement of batteries is challenging. For these microelectronic and IoT devices the abundant energy harvester is very useful. Among different abundant energy resources, vibrational energy harvesting with piezoelectric cantilever beam energy harvesters is of interest. This research work presents the design and analysis of an energy harvester (EH) which contains a single piezoelectric cantilever beam that captures the vibrational energy of the suspension bridge. This approach ties the two things together by framing piezoelectric energy harvesting as a solution to the power challenges faced by low-powered devices, making the transition feel more natural and connected. The main challenge in the design was matching the resonance frequency of the bridge with a piezo EH which is around 2.5Hz to extract maximum power. To overcome this problem Eigen frequency analysis in COMSOL Multiphysics is done. The 3D geometry of single beam piezo EH is designed and analyzed in COMSOL Multiphysics solid works. In this research work a relationship is established between the geometrical parameters of the single beam piezo EH and Eigen frequency based on the first six Eigen frequency analyses in COMSOL Multiphysics. For fi nite element analysis(FEA) a piezo single beam harvester is vibrated by application of force which is equal to the vibrational force (0.98m/s<sup>2</sup>) in the suspension bridge. The force of (0.98 m/s²) is chosen because it avoids resonating with critical system components. The output from the harvester is achieved at a resonance frequency of 2.5Hz. The output from the piezo is very low 800 milli volts at 2.5Hz. The output results of piezo EH are also compared with a cantilever beam with a single-branch structure.

Subattojoule Electrical Switching in a Two-Dimensional TaSe2 Oxide Device with High Endurance.

Recent developments in artificial intelligence and the internet-of-things have created great demand for low-power microelectronic devices. Two-dimensional (2D) electrical switching materials are extensively used in neuromorphic computing technology, yet their high leakage current and low endurance impede their further application. This study presents a vertical crossbar-structured conductive-bridge threshold switching device based on 2D TaSe2 oxide. Utilizing natural oxidation under air to generate a TaSeO functional layer, this device demonstrates stable electrical switching behavior with a low operating voltage (<0.8 V) and a steep turn-on slope (<9.4 mV decade-1). Notably, the device demonstrates exceptionally subattojoule energy consumption (0.142 aJ) and remarkable durability (>108). This breakthrough tackles the endurance issues prevalent in 2D devices and holds promising implications for neuromorphic computing. The fundamental switching process, as supported by transmission electron microscopy, hinges on the role of Ag2Se nanocrystalline islands in promoting the growth of conductive filaments, enhancing device performance and endurance.

Ultrahigh Exchange Bias Field/Coercive Field Ratio in In Situ Formed Two-Dimensional Magnetic Te-Cr2O3/Cr5Te6 Heterostructures.

The exchange bias (EB) effect is a fundamental magnetic phenomenon, in which the exchange bias field/coercive field ratio (|HEB/HC|) can improve the stability of spintronic devices. Two-dimensional (2D) magnetic heterostructures have the potential to construct low-power and high-density spintronic devices, while their typically air unstable and |HEB/HC| lesser, limiting the possibility of applications. Here, 2D Cr5Te6 nanosheets have been systematically synthesized with an in situ formed ≈2 nm-thick Te doped Cr2O3 layer (Te-Cr2O3) on the upper surface by chemical vapor deposition (CVD)method. The strong and air stable EB effect, achieving a |HEB/HC| of up to 80% under an ultralow cooling field of 0.01 T, which is greater than that of the reported 2D magnetic heterostructures. Meanwhile, the uniformity of thickness and chemical composition of the Te-Cr2O3 layer can be controlled by the growth conditions which are highly correlated with the EB effect of 2D Te-Cr2O3/Cr5Te6 heterostructures. First-principles calculations show that the Te-Cr2O3 can provide uncompensated spins in the Cr2O3, thus forming strong spin pinning effect. The systematical investigation of the EB effect in 2D Te-Cr2O3/Cr5Te6 heterostructures with high |HEB/HC| will open up exciting opportunities in low-power and high-stability 2D spintronic devices.

Integrating control-theoretic predictive deep learning for resource-efficient computing systems

Abstract Deep learning (DL) powers numerous applications on ubiquitous edge devices, but its high resource demands pose a challenge. Approximate computing is often proposed to alleviate this, yet such calculation usually suffers from a fixed level of accuracy loss. We propose a novel control-theoretic approach for predictive DL inference on resource constrained devices. Our system dynamically adjusts approximation levels based on a trade-off between resource utilisation and accuracy, considering future demands. Extensive experiments across diverse domains - human activity recognition, acoustic scene profiling, and computer vision - with various neural network architectures and approximation techniques, demonstrate that our approach achieves up to 50% energy savings while maintaining the desired inference accuracy and incurring minimal runtime overhead. Furthermore, we showcase our method in a real-world deployment on low-power edge devices and confirm its superiority over current state-of-the-art solutions.

Ultrathin Rare-Earth Oxyhalides as High-κ van der Waals Layered Dielectrics.

Van der Waals (vdW) dielectrics are extensively employed to enhance the performance of 2D electronic devices. However, current vdW dielectric materials still encounter challenges such as low dielectric constant (κ) and difficulties in synthesizing high-quality single crystals. 2D rare-earth oxyhalides (REOXs) with exceptional electrical properties present an opportunity for the exploration of novel high-κ dielectrics. In this study, for the first time, the synthesis of a series of van der Waals layered gadolinium oxyhalides with thicknesses down to monolayer through a space-confined vdW epitaxy approach and demonstrating their application as a single-crystalline gate dielectric is reported. It exhibits a remarkable relative dielectric constant exceeding 17 and an impressive breakdown field strength of 13.5MVcm-1. The 2D transistors directly gated by the REOXs layer exhibit enhanced electron mobility and a low interface trap density. An ultrahigh on/off current ratio of 109 and a near-Boltzmann-limit subthreshold swing is achieved. The superior dielectric properties, combined with the universality and scalability of the production method (e.g., millimeter-scale films are achieved), demonstrate that 2D REOXs can serve as promising gate dielectrics for 2D electronics, thereby expanding the study of high-κ vdW materials and potentially providing new opportunities for the development of low-power electronic devices.

Microalga-Based Electricity Production: A Comprehensive Review

This review evaluates the feasibility of using microalgal culture for sustainable energy production, emphasizing microbial fuel cells (MFCs) and biophotovoltaics (BPVs). This study’s uniqueness is rooted in its thorough examination of recent developments (2014–present) in microalgal strain selection, bioreactor design, and electrode materials. Furthermore, this review combines microalga cultivation with wastewater treatment, highlighting its importance. Notably, it examines advanced methodologies, such as the use of genetic engineering to enhance microalgal traits, nanotechnology to optimize electrode efficacy, and artificial intelligence (AI) to optimize bioelectrochemical systems. In addition, this study identifies possible future research avenues by examining microalga–bacterium consortia and cascaded biobattery systems. Consequently, the incorporation of case studies illustrating microalga biobatteries’ practical applications in low-power devices and wastewater treatment underscores the technology’s promise. Similarly, this study examines significant problems with enhancing farming methods, reconciling cost and yield, and integrating renewable energy sources with the grid, offering vital insights for academics and policymakers. Ultimately, this review emphasizes the need for economical cultivation methods, waste stream utilization, and scalable bioreactor designs, thereby considerably advancing sustainable energy options.

Seizure detection via reservoir computing in MoS2-based charge trap memory devices.

Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide. Neuromorphic computing, inspired by the human brain, demonstrated capabilities of on-chip computing with low power consumption. In this context, bidimensional (2D) semiconductors hold notable promise, thanks to their unique electronic properties, atomic-scale thickness, and scalability, making them ideal for low-power applications. This work presents a neuromorphic reservoir computing system exploiting MoS2-based charge trap memories (CTMs) for processing of electrophysiological signals. Real-time seizures detection is achieved, thanks to the nonlinear integration of local-field potential (LFP) recorded from in vitro rodent models of ictogenesis. The results support MoS2-based CTMs for low-power biomedical devices in clinical diagnosis and treatment of epilepsy.

Nonvolatile Memory Device Based on the Ferroelectric Metal/Ferroelectric Semiconductor Junction.

The ferroelectric tunnel junction (FTJ) is a competitive candidate for post-Moore nonvolatile memories due to its low power consumption and nonvolatility, with its performance being strongly dependent on the conditions for contact between the ferroelectric material and the metal electrode. The development of two-dimensional materials in recent years has offered new opportunities such as functional metal layers, which is challenging for traditional FTJ systems. Here, we introduce the newly discovered ferroelectric metal WTe2 as the electrode to construct WTe2/α-In2Se3/Au ferroelectric semiconductor junctions. The interplay between the ferroelectricity in the van der Waals electrode and tunnel junction leads to the emergence of novel device characteristics, including concomitant multiresistance levels, a low switching voltage (<2 V), and a high on/off ratio (>105). Using ferroelectric metal as the electrode for the ferroelectric tunnel/semiconductor junction offers an alternative approach to low-power and high-density ferroelectric memory devices.

Observation of Large Low-Field Magnetoresistance in Layered (NdNiO3)n:NdO Films at High Temperatures.

Large low-field magnetoresistance (LFMR, <1 T), related to the spin-disorder scattering or spin-polarized tunneling at boundaries of polycrystalline manganates, holds considerable promise for the development of low-power and ultrafast magnetic devices. However, achieving significant LFMRtypically necessitates extremely low temperatures due to diminishing spin polarization as temperature rises. To address this challenge, one strategy involves incorporating Ruddlesden-Popper structures (ABO3)n:AO, which are layered derivatives of perovskite structure capable of potentially inducing heightened magnetic fluctuations at higher temperatures. Here, a remarkable LFMR of up to 1.0×103% is obtained in the layered (NdNiO3)n:NdO films with a high and wide temperature range (190-240 K). This finding underlines that the layered (NdNiO3)n:NdO (n = 1) structure show a complex magnetic structure above TMI of perovskite NdNiO3, where small ferromagnetic domains are embedded in the antiferromagnetic domains, raising the tunneling barriers and magnetic fluctuations at high temperatures. Furthermore, applying a low magnetic field (<0.1 T) near TMI effectively mitigates the disruption of antiferromagnetic order structures at boundaries, then a higher temperature is required to break the inhibition of ferromagnetic to antiferromagnetic phase transition. The results contribute significantly to the advancement of magnetic devices capable of achieving substantial LFMR at room temperature.

Feature enhanced cascading attention network for lightweight image super-resolution

Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA). Specifically, ESA enhances high-frequency texture features in the feature maps, and MLSKA executes the further extraction. The rich and fine-grained high-frequency information are extracted and fused from multiple perceptual layers, thus improving super-resolution (SR) performance. To validate FECAN’s effectiveness, we evaluate it with different complexities by stacking different numbers of high-frequency enhancement modules (HFEM) that contain FECA. Extensive experiments on benchmark datasets demonstrate that FECAN outperforms state-of-the-art lightweight SR networks in terms of objective evaluation metrics and subjective visual quality. Specifically, at a × 4 scale with a 121 K model size, compared to the second-ranked MAN-tiny, FECAN achieves a 0.07 dB improvement in average peak signal-to-noise ratio (PSNR), while reducing network parameters by approximately 19% and FLOPs by 20%. This demonstrates a better trade-off between SR performance and model complexity.

Multi-agent Deep Reinforcement Learning-based Key Generation for Graph Layer Security

Recently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this paper, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), etc.

Boosting Energy Harvesting Performance in Piezoelectric Composites with Aligned Porosity via a Dual Structure Design Strategy.

Porous piezoelectric materials have attracted much interest in the fields of sensing and energy harvesting owing to their low dielectric constant, high piezoelectric voltage coefficient, and energy harvesting figure of merit. However, the introduction of porosity can decrease the piezoelectric coefficient, which restricts the enhancement of output current and power density. Herein, to overcome these challenges, an array-structured piezoelectric composite energy harvester with aligned porosity was constructed via a dual structure design strategy to enhance the output current and power density. Silver metal particles were introduced into a porous barium calcium zirconate titanate (BCZT) ceramic matrix as a secondary phase to regulate the dielectric, ferroelectric, and piezoelectric properties. The optimal addition of silver particles can reduce the size of ferroelectric domains, which leads to an enhanced piezoelectric coefficient and energy harvesting figure of merit. Combined with the design of the array structure, the maximum output voltage and current of the piezoelectric composite energy harvester can reach 76 V and 340 μA, respectively, with a peak power density of 0.97 mW/cm2. Furthermore, the array-structured piezoelectric energy harvester can light 40 blue LED bulbs and power commercial low-power electronic devices. This work provides a strategy to boost the output current and power density of porous piezoelectric materials via a micro-macro structure design strategy for energy harvesting applications.

A self-sustained moist-electric generator with enhanced energy density and longevity through a bilayer approach.

Although MEG is being developed as a green renewable energy technology, there remains significant room for improvement in self-sustained power supply, generation duration, and energy density. In this study, we present a self-sustained, high-performance MEG device with a bilayer structure. The lower hydrogel layer incorporates graphene oxide (GO) and carbon nanotubes (CNTs) as the active materials, whereas the upper aerogel layer is comprised of pyrrole-modified graphene oxide (PGO). This design generates a dual-gradient structure (ion density gradient and relative humidity gradient), enabling continuous power generation from the intrinsic moisture in the hydrogel. The device can operate for up to 16 days without external water and extend its operation to 45 days with added moisture. Remarkably, encapsulating this MEG maintains its high performance output even after nearly three months. The short-circuit current of MEG reaches 1695 μA, with an energy density of 809.2 μW h cm-2, which is considerably higher than those reported in previous studies on MEG. This work highlights a promising approach for long-term, self-sustained power generation, with potential applications in environmental sensing and low-power devices.

Multifunctional Organic Molecule for Defect Passivation of Perovskite for High-Performance Indoor Solar Cells.

Perovskite solar cells (PSCs) can utilize the residual photons from indoor light and continuously supplement the energy supply for low-power electron devices, thereby showing the great potential for sustainable energy ecosystems. However, the solution-processed perovskites suffer from serious defect stacking within crystal lattices, compromising the low-light efficiency and operational stability. In this study, we designed a multifunctional organometallic salt named sodium sulfanilate (4-ABS), containing both electron-donating amine and sulfonic acid groups to effectively passivate the positively-charged defects, like under-coordinated Pb ions and iodine vacancies. The strong chemical coordination of 4-ABS with the octahedra framework can further regulate the crystallization kinetics of perovskite, facilitating the enlarged crystal sizes with mitigated grain boundaries within films. The synergistic optimization effects on trap suppression and crystallization modulation upon 4-ABS addition can reduce energy loss and mitigate ionic migration under low-light conditions. As a result, the optimized device demonstrated an improved power conversion efficiency from 22.48% to 24.34% and achieved an impressive efficiency of 41.11% under 1000 lux weak light conditions. This research provides an effective defect modulation strategy for synergistically boosting the device efficiency under standard and weak light irradiations.

Gate-Controlled Superconducting Switch in GaSe/NbSe2 van der Waals Heterostructure.

The demand for low-power devices is on the rise as semiconductor engineering approaches the quantum limit, and quantum computing continues to advance. Two-dimensional (2D) superconductors, thanks to their rich physical properties, hold significant promise for both fundamental physics and potential applications in superconducting integrated circuits and quantum computation. Here, we report a gate-controlled superconducting switch in GaSe/NbSe2 van der Waals (vdW) heterostructure. By injecting high-energy electrons into NbSe2 under an electric field, a non-equilibrium state is induced, resulting in significant modulation of the superconducting properties. Owing to the intrinsic polarization of ferroelectric GaSe, a much steeper subthreshold slope and asymmetric modulation are achieved, which is beneficial for the device performance. Based on these results, a superconducting switch is realized that can reversibly and controllably switch between the superconducting and normal states under an electric field. Our findings highlight the significant high-energy injection effect from band engineering in 2D vdW heterostructures combining superconductors and ferroelectric semiconductors and demonstrate the potential for applications in superconducting integrated circuits.

Neural network quantification for solar radiation prediction: An approach for low power devices

Accurate solar radiation prediction leverages various machine learning techniques, with artificial neural networks (ANN) being the most common and precise due to their ability to detect and learn relationships between meteorological variables and solar radiation. Traditionally, training and deploying these models require high-capacity computers. However, the proliferation of low-power smart devices, such as embedded systems and mobile devices, necessitates exploring methodologies for implementing ANN on systems with limited computational resources. This paper proposes a quantized neural network model for solar radiation prediction, considering the hardware limitations of low-power devices like the Raspberry Pi RP2040 microcontroller. The methodology involves five stages: hardware and software selection, neural network development and quantization, microcontroller implementation, model validation, and result analysis. Experimental design allows detailed performance evaluation of quantized neural networks, demonstrating that the TensorFlow Lite Quantized Aware model is suitable for solar radiation prediction. Metrics such as root mean square error (RMSE) of 44.24 and R² of 0.96 indicate that the selected quantized model differs from the original non-quantized model by less than 0.5% in RMSE and 0.04% in R². The study concludes that implementing quantized ANN models on microcontrollers is a technically and economically viable solution for solar radiation prediction. Quantization enables complex predictive models to run on low-cost, energy-efficient devices, thereby democratizing advanced prediction technologies for critical applications like solar energy generation.

Design of Multilayered XOR Gate Using Quantum Dot Cellular Automata

The rapid advancement of microelectronics technology necessitates the development of high-speed, low-power devices. Quantum Cellular Automaton (QCA) is emerging as a promising technology enabling logic circuits with exceptional operating speeds. The XOR gate is crucial in nanocomputing applications, such as nano-processors and nano-communication units, and requires innovative design approaches. This paper presents a novel design of an XOR gate using a multilayered layout methodology within the QCA framework. The circuit’s design and validation are performed using QCADesigner 2.0.3, while energy dissipation analysis is conducted with QCADesigner-E, resulting in an energy dissipation of 15 meV. Comprehensive parameter analysis, including cost functions, highlights the superiority of the proposed design. A comparative analysis with similar existing multilayered designs shows a 55% improvement in QCA-specific quantum cost. Notably, the proposed design eliminates the need for internal nodes within the layout, enhancing the methodology’s applicability to higher-order and more complex circuits.