Circuit Complexity Research Articles

Simulation and fabrication of a 20 kHz single-beam transducer

Abstract With the vigorous development of underwater acoustic technology in China, underwater acoustic transducers are constantly moving towards broadband, low-frequency, and high-power directions. How to obtain piezoelectric transducers with lower frequencies is still a research direction that scholars from various countries are striving to study. This article mainly studies and manufactures a 20 kHz single-beam piezoelectric transducer, using the classic Tonpilz transducer structure. Firstly, the Tonpilz transducer is theoretically analyzed using the classical equivalent circuit method. Secondly, the transducer is analyzed using ANSYS finite element analysis software to study the influence of piezoelectric ceramics on the frequency of the transducer, and the size of the piezoelectric ceramics is optimized to determine the size of the piezoelectric ceramic structure of the transducer. The working frequency of the transducer was calculated using software, and it was determined that the working frequency was approximately 20 kHz.

Investigation of the TaOx unipolar switching memory on high efficiency computing

Memristor has shown great potential application for data storage, neuromorphic computing chip, and memory logic, but its bi-directional operation from setting in positive bias voltage region to resetting in a negative bias voltage region would introduce a complex control circuit. Herein, a memristor with the structure of Au/TaOx/F-doped SnO2 presents unipolar switching behavior, enabling the memristor to operate the set and reset processing in a single voltage direction. By comparison with bipolar switching memristor, using this unipolar switching memristor to operate the logic expression of the letter “D” based on American standard code for information interchange (ASCII) can reduce power consumption by 55.56% while switching the logic state from “0” to “1” and then back to “0” can save up to 48% of power consumption. This work provides a feasible method for the low power consumption computing as well as a road to simplify circuit design.

High efficiency AOT-controlled boost converter with pseudo-constant switching frequency

An on-time generator for boost converters is proposed to achieve an adaptive on-time (AOT) control with pseudo-constant frequency in continuous conduction mode (CCM). Since the first-order parasitic effect of the components and devices is considered in the design of the on-time generator, the switching frequency (fsw) variation of the boost converter in CCM can be better suppressed with the load current (ILOAD), input voltage (VIN) and output voltage (VOUT) changes, which can effectively solve the EMI noise problem. In addition, the idea of capacitor pre-charging is applied to simplifying circuit complexity. Meanwhile, an on-demand modulation strategy is applied to improve the conversion efficiency. Simulation results show that the boost converter based on the proposed on-time generator can realize the fsw variation of 0.4 % in case of 200 mA ILOAD change. Moreover, the VIN may range from 0.8 to 1.5 V, the VOUT is set to 1.8 V, and the peak efficiency of the simulation is 96.9 %.

CMOS-Compatible High-Performance Silicon Nanowire Array Natural Light Electronic Detection System.

In this article, we propose a novel natural light detector based on high-performance silicon nanowire (SiNW) arrays. We achieved a highly controllable and low-cost fabrication of SiNW natural light detectors by using only a conventional micromachined CMOS process. The high activity of SiNWs leads to the poor long-term stability of the SiNW device, and for this reason, we have designed a fully wrapped structure for SiNWs. SiNWs are wrapped in transparent silicon nitride and silicon oxide films, which greatly improves the long-term stability of the detector; at the same time, this structure protects the SiNWs from breakage. In addition, the SiNW arrays are regularly distributed on the top of the detector, which can quickly respond to natural light. The response time of the detector is about 0.015 s. Under the illumination of 1 W·m-2 light intensity, multiple SiNWs were detected together. The signal strength of the detector reached 1.82 μA, the signal-to-noise ratio was 47.6 dB, and the power consumption was only 0.91 μW. The high-intensity and highly reliable initial signal reduces the cost and complexity of the backend signal processing circuit. A low-cost and high-performance STM32 microcontroller can realize the signal processing task. Therefore, we built a high-performance SiNW natural optoelectronic detection system based on an STM32 microcontroller, which achieved the real-time detection of natural light intensity, with an accuracy of ±0.1 W·m-2. These excellent test performances indicate that the SiNW array natural light detector in this article meey the requirements of practicality and has broad potential for application.

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and Processing.

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing challenges; however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing.

Large circuit models: opportunities and challenges

Within the electronic design automation (EDA) domain, artificial intelligence (AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level (RTL) designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems’ capabilities.

Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits

The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central bottleneck in the scalability of variational quantum algorithms (VQAs), and are known to arise in various ways. However, a caveat of most existing gradient bound results is the requirement of t-design circuit assumptions that are typically not satisfied in practice. In this work, we loosen these assumptions altogether and derive tight upper and lower bounds on loss and gradient concentration for a large class of parameterized quantum circuits and arbitrary observables, which are significantly stronger than prior work. Moreover, we show that these bounds, as well as the variance of the loss itself, can be estimated efficiently and classically-providing practical tools to study the loss landscapes of VQA models, including verifying whether or not a circuit/observable induces barren plateaus. In particular, our results can readily be leveraged to rule out barren plateaus for a realistic class of ansätze and mixed observables, namely, observables containing a non-vanishing local term. This insight has direct implications for hybrid Quantum Generative Adversarial Networks (qGANs). We prove that designing the discriminator appropriately leads to 1-local weights that stay constant in the number of qubits, regardless of discriminator depth. This implies that qGANs with appropriately chosen generators do not suffer from barren plateaus even at scale-making them a promising candidate for applications in generative quantum machine learning. We demonstrate this result by training a qGAN to learn a 2D mixture of Gaussian distributions with up to 16 qubits, and provide numerical evidence that global contributions to the gradient, while initially exponentially small, may kick in substantially over the course of training.

On the Exploration of Quantum Polar Stabilizer Codes and Quantum Stabilizer Codes with High Coding Rate.

Inspired by classical polar codes, whose coding rate can asymptotically achieve the Shannon capacity, researchers are trying to find their analogs in the quantum information field, which are called quantum polar codes. However, no one has designed a quantum polar coding scheme that applies to quantum computing yet. There are two intuitions in previous research. The first is that directly converting classical polar coding circuits to quantum ones will produce the polarization phenomenon of a pure quantum channel, which has been proved in our previous work. The second is that based on this quantum polarization phenomenon, one can design a quantum polar coding scheme that applies to quantum computing. There are several previous work following the second intuition, none of which has been verified by experiments. In this paper, we follow the second intuition and propose a more reasonable quantum polar stabilizer code construction algorithm than any previous ones by using the theory of stabilizer codes. Unfortunately, simulation experiments show that even the stabilizer codes obtained from this more reasonable construction algorithm do not work, which implies that the second intuition leads to a dead end. Based on the analysis of why the second intuition does not work, we provide a possible future direction for designing quantum stabilizer codes with a high coding rate by borrowing the idea of classical polar codes. Following this direction, we find a class of quantum stabilizer codes with a coding rate of 0.5, which can correct two of the Pauli errors.

High‐Performance, Single‐Component Ambipolar Organic Electrochemical Transistors with Balanced n/p‐Type Properties for Inverter and Biosensor Applications

AbstractAmbipolar organic electrochemical transistors (OECTs) with a single organic mixed ionic‐electronic conductor (OMIEC) have unique advantages by reducing fabrication complexity and cost in complementary logic circuit and bioelectronic applications. However, the design and synthesis of high‐performance ambipolar OMIECs for efficient transport and coupling both cation/anion and electron/hole remain challenging. Herein, a donor–acceptor (D–A)‐typed ambipolar OMIEC polymer (DHF‐gTT) is presented, whose superior ambipolarity stems from the concurrent oligoethyleneglycol‐functionalized D/A units that facilitates cation/anion injection and transport. Additionally, the fluorination of acceptor unit improves both hole/electron transports due to the fully‐locked backbone and promotes electron injection by down‐shifting molecular energy levels. Consequently, DHF‐gTT‐based OECTs achieve balanced figure‐of‐merit (µC*) for both p‐type and n‐type operations (12.4–14.6 F cm−1 V−1 s−1), setting new benchmarks for ambipolar OECTs, in terms of n‐type µC* and electron/hole mobilities. Such ambipolar OECTs enable the inverter to achieve a record‐high gain (102 V/V) and associated n‐type and p‐type molecular detectors to offer real‐time and highly sensitive detection of histamine and hydrogen peroxide detection, respectively. These results indicate the effectiveness of judicious molecular design on achieving high‐performance ambipolar OMIECs, allowing the state‐of‐the‐art single‐component ambipolar OECTs for both highly integrated logic circuit and bioelectronic applications.

Brick wall quantum circuits with global fermionic symmetry

We study brick wall quantum circuits enjoying a global fermionic symmetry. The constituent 2-qubit gate, and its fermionic symmetry, derive from a 2-particle scattering matrix in integrable, supersymmetric quantum field theory in 1+1 dimensions. Our 2-qubit gate, as a function of three free parameters, is of so-called free fermionic or matchgate form, allowing us to derive the spectral structure of both the brick wall unitary U_FUF and its, non-trivial, Hamiltonian limit H_{\gamma}Hγ in closed form. We find that the fermionic symmetry pins H_{\gamma}Hγ to a surface of critical points, whereas breaking that symmetry leads to non-trivial topological phases. We briefly explore quench dynamics for this class of circuits.

Simulating Noisy Variational Quantum Algorithms: A Polynomial Approach.

Large-scale variational quantum algorithms are widely recognized as a potential pathway to achieve practical quantum advantages. However, the presence of quantum noise might suppress and undermine these advantages, which blurs the boundaries of classical simulability. To gain further clarity on this matter, we present a novel polynomial-scale method based on the path integral of observable's backpropagation on Pauli paths (OBPPP). This method efficiently approximates expectation values of operators in variational quantum algorithms with bounded truncation error in the presence of single-qubit Pauli noise. Theoretically, we rigorously prove: (i)For a constant minimal nonzero noise rate γ, OBPPP's time and space complexity exhibit a polynomial relationship with the number of qubits n, the circuit depth L. (ii)For variable γ, in scenarios where more than two nonzero noise factors exist, the complexity remains Poly(n,L) if γ exceeds 1/logL, but grows exponential with L when γ falls below 1/L. Numerically, we conduct classical simulations of IBM's zero-noise extrapolated experimental results on the 127-qubit Eagle processor [Y. Kim et al., Evidence for the utility of quantum computing before fault tolerance, Nature (London) 618, 500 (2023).NATUAS0028-083610.1038/s41586-023-06096-3]. Our method attains higher accuracy and faster runtime compared to the quantum device. Furthermore, our approach allows us to simulate noisy outcomes, enabling accurate reproduction of IBM's unmitigated results that directly correspond to raw experimental observations. Our research reveals the vital role of noise in classical simulations and the derived method is general in computing the expected value for a broad class of quantum circuits and can be applied in the verification of quantum computers.

On complexity of colloid cellular automata

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time complexity of Boolean circuits derived from the electrical responses of colloids-specifically ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and their combinations in response to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata-arrays of binary state machines that update their states synchronously according to the same rule-creating the colloid cellular automata. We then analyze the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

Sensation is Dispensable for the Maturation of the Vestibulo-ocular Reflex.

Vertebrates stabilize gaze using a neural circuit that transforms sensed instability into compensatory counter-rotation of the eyes. Sensory feedback tunes this vestibulo-ocular reflex throughout life. Gaze stabilization matures progressively, either due to similar tuning, or to a slowly developing circuit component. Here we studied the functional development of vestibulo-ocular reflex circuit components in the larval zebrafish, with and without sensation. Blind fish stabilize gaze normally, and neural responses to body tilts mature before behavior. Instead, synapses between motor neurons and the eye muscles mature with a timecourse similar to behavioral maturation. Larvae without vestibular sensory experience, but whose neuromuscular junction was mature, had a strong vestibulo-ocular reflex. Development of the neuromuscular junction, and not sensory experience, determines the rate of maturation of an ancient behavior.

Evaluating the Contribution of Model Complexity in Predicting Robustness in Synthetic Genetic Circuits.

The design-build-test-learn workflow is pivotal in synthetic biology as it seeks to broaden access to diverse levels of expertise and enhance circuit complexity through recent advancements in automation. The design of complex circuits depends on developing precise models and parameter values for predicting the circuit performance and noise resilience. However, obtaining characterized parameters under diverse experimental conditions is a significant challenge, often requiring substantial time, funding, and expertise. This work compares five computational models of three different genetic circuit implementations of the same logic function to evaluate their relative predictive capabilities. The primary focus is on determining whether simpler models can yield conclusions similar to those of more complex ones and whether certain models offer greater analytical benefits. These models explore the influence of noise, parametrization, and model complexity on predictions of synthetic circuit performance through simulation. The findings suggest that when developing a new circuit without characterized parts or an existing design, any model can effectively predict the optimal implementation by facilitating qualitative comparison of designs' failure probabilities (e.g., higher or lower). However, when characterized parts are available and accurate quantitative differences in failure probabilities are desired, employing a more precise model with characterized parts becomes necessary, albeit requiring additional effort.

Orexin improves chronic restraint stress induced depressive-like behavior via modulating the lateral septum in mice

The orexin system participates in the regulation of depression; however, its effects show significant heterogeneity, indicating the involvement of complex downstream neural circuit mechanisms. The lateral septum (LS), located downstream of the orexin system, contributes to depression. However, the effects and mechanisms underlying the orexin-mediated modulation of the LS in patients with depression remain unclear. Herein, we applied fiber photometry, chemogenetics, neuropharmacology, and in vitro electrophysiology to show that LS orexinergic afferents are sensitive to acute restraint and that chronic restraint stress (CRS) inhibits LS-projecting orexin neurons. Chemogenetic activation of LS orexinergic afferents or injection of orexin-A into the LS improved CRS-induced depression-like behavior. In vitro perfusion of orexin-A increased the action potential of somatostatin neurons in the LS. Overall, this study provides evidence that orexin improves depressive-like behavior by modulating the LS, and that this effect is probably mediated by the upregulation of LS somatostatin neurons.

Stretchable Arduinos embedded in soft robots.

To achieve real-world functionality, robots must have the ability to carry out decision-making computations. However, soft robots stretch and therefore need a solution other than rigid computers. Examples of embedding computing capacity into soft robots currently include appending rigid printed circuit boards to the robot, integrating soft logic gates, and exploiting material responses for material-embedded computation. Although promising, these approaches introduce limitations such as rigidity, tethers, or low logic gate density. The field of stretchable electronics has sought to solve these challenges, but a complete pipeline for direct integration of single-board computers, microcontrollers, and other complex circuitry into soft robots has remained elusive. We present a generalized method to translate any complex two-layer circuit into a soft, stretchable form. This enabled the creation of stretchable single-board microcontrollers (including Arduinos) and other commercial circuits (including SparkFun circuits), without design simplifications. As demonstrations of the method's utility, we embedded highly stretchable (>300% strain) Arduino Pro Minis into the bodies of multiple soft robots. This makes use of otherwise inert structural material, fulfilling the promise of the stretchable electronic field to integrate state-of-the-art computational power into robust, stretchable systems during active use.

Hitting Sets for Orbits of Circuit Classes and Polynomial Families

The orbit of an n -variate polynomial \(f({\mathbf {x}})\) over a field \(\mathbb {F}\) is the set \(\text {orb}(f) := \lbrace f(A{\mathbf {x}}+{\mathbf {b}}) : A \in \mathrm{GL}(n,\mathbb {F}) \text{ and } {\mathbf {b}}\in \mathbb {F}^n\rbrace\) . The orbit of a polynomial f is a geometrically interesting subset of the set of affine projections of f . Affine projections of polynomials computable by seemingly weak circuit classes can be quite powerful. For example, the polynomial \(\mathsf {IMM}_{3,d}\) —the \((1,1)\) th entry of a product of d generic \(3 \times 3\) matrices—is computable by a constant-width read-once oblivious algebraic branching program (ROABP), yet every polynomial computable by a size- s general arithmetic formula is an affine projection of \(\mathsf {IMM}_{3,\text {poly}(s)}\) as shown by Ben-or and Cleve [ 12 ]. To our knowledge, no efficient hitting set construction was known for \(\text {orb}(\mathsf {IMM}_{3, d})\) before this work. In this article, we initiate the study of explicit hitting sets for the orbits of polynomials computable by several natural and well-studied circuit classes and polynomial families. In particular, we give quasi-polynomial time hitting sets for the orbits of: Low-individual-degree polynomials computable by commutative ROABPs . This implies quasi-polynomial time hitting sets for the orbits of the elementary symmetric polynomials and the orbits of multilinear sparse polynomials . Multilinear polynomials computable by constant-width ROABPs . This implies a quasi-polynomial time hitting set for the orbits of the family \(\lbrace \mathsf {IMM}_{3,d}\rbrace _{d \in \mathbb {N}}\) . Polynomials computable by constant-depth, constant-occur formulas . This implies quasi-polynomial time hitting sets for the orbits of multilinear depth-4 circuits with constant top fan-in , and also polynomial-time hitting sets for the orbits of the power symmetric polynomials and the sum-product polynomials . Polynomials computable by occur-once formulas . We say a polynomial has low individual degree if the degree of every variable in the polynomial is at most \(\text {poly}(\log n)\) , where n is the number of variables. The first two results are obtained by building upon and strengthening the rank concentration by translation technique of Agrawal, Saha, and Saxena [ 6 ]; the second result additionally uses the merge-and-reduce idea from Forbes and Shpilka [ 30 ] and Forbes, Shpilka, and Saptharishi [ 27 ]. The proof of the third result adapts the algebraic independence-based technique of Agrawal, Saha, Saptharishi, and Saxena [ 5 ] and Beecken, Mittmann, and Saxena [ 11 ] to reduce to the case of constructing hitting sets for the orbits of sparse polynomials. A similar reduction using the Shpilka-Volkovich (SV) generator-based argument in Shpilka and Volkovich [ 90 ] yields the fourth result. The SV generator plays an important role in all four results.

A robust color image encryption scheme with complex whirl wind spiral chaotic system and quadrant-wise pixel permutation

This paper presents a novel encryption technique that uses a unique chaotic circuit design called as 3D Complex Whirl Wind Spiral chaotic system (CWWS). The major goal of this novel approach is to create an efficient 3D chaotic systems with increased randomness and multistability, specifically designed to encrypt multimedia data. The design incorporates the sine function sin(x) to introduce complexity and unpredictability in the chaotic circuit. The dynamic behaviour of the proposed scheme’s chaotic system is thoroughly evaluated using a variety of analyses, including KY dimension, dissipativity, Lyapunov exponent spectra, and bifurcation diagrams. There are two key stages to the encryption process: diffusion and confusion. The diffusion process is strengthened by the smooth integration of quadrant-wise pixel permutation (QWPP) algorithms, which eliminate correlations between neighbouring pixels. Following that, the image components are concealed using the chaotic sequence that was generated from the 3D CWWS chaotic system. The complete encrypted image is then created by combining these encrypted components. The simulation results of the proposed strategy are thoroughly investigated using statistical analysis, differential analysis, and brute force attacks. The system has optimal key space, entropy, UACI, and NPCR metric values of 2400, 7.99, 0.334, and 0.996, respectively. Furthermore, the experimental findings show robust resistance to statistical, differential, and brute force attacks for a single round of iteration.

Quasi-passive modulation for monolithic GaN photoelectronic circuit

Due to the overlap between the electroluminescence spectrum and spectral responsivity curve, gallium nitride (GaN)-based multi-quantum well (MQW) diodes can modulate and detect light emitted by another diode with the same MQW structure. This enables the realization of a monolithic integrated GaN optoelectronic circuit, integrating an MQW-based transmitter, waveguide, modulator, and receiver on a tiny GaN chip. It is well known that the active region of MQW absorbs high-energy photons within the plane, generating electron-hole pairs and forming photogenerated carriers. The change in free carrier concentration causes variations in the refractive index and absorption characteristics of the waveguide, thereby manipulating light propagation within the waveguide. Based on this physical phenomenon, a quasi-passive modulation scheme is proposed for a monolithic GaN photoelectronic circuit. This scheme achieves optical modulation by connecting a variable resistor between the modulator electrodes, avoiding the need for high-precision external electric fields and complex control circuits. The performance of the quasi-passive modulation was tested through on-chip data transmission and real-time audio signal transmission. The results indicate that quasi-passive modulation is highly feasible in optoelectronic systems and shows great promise as a competitive core module for future large-scale photonic integrated circuits.

Geometrically asymmetric pressure sensor with precious few electrodes achieves precise detection of both position and strength by machine learning

Wearable devices that can accurately detect the position and strength of applied pressure have been a focus of attention in the field of flexible tactile sensing. However, conventional sensor arrays with multiple units have many challenges in practical applications due to their complex circuit layouts and poor robustness, etc., which could be solved by minimizing the number of connecting electrodes among each unit. Herein, a pressure sensor with geometrically asymmetric structure has been proposed to remove all the connected inner electrodes. The sensor is constructed by three pieces of fabrics, with one insulating fabric sandwiched by two conductive fabrics coated with carbon nanotubes. Notably, the bottom fabric is specially designed in an optimized asymmetric shape with a rectangular hole located off-center of the square, assisting the sensor to generate different resistive and capacitive responses among pre-divided nine areas through only three external electrodes. Compared to conventional resistive sensor arrays, the number of electrodes is reduced by 83.3 %. With the assistance of machine learning, pressure-positioning with an accuracy of more than 98.1 % and precise strength measurements are achieved. Based on this sensor and the trained neural network, a two-level password system is constructed for more reliable role recognition. The asymmetric strategy proposed in this work provides a novel design for realizing precise detection of both position and strength using extremely simplified circuits, which has great potential in the field of wearable electronics.