Circuit Complexity Research Articles

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.

Another Evidence to not Employ Customized Masked Hardware

As a well-studied countermeasure against side-channel analysis attacks, there is a general interest in applying masking to different cryptographic functions executed on different platforms. On the one hand, despite their high performance, masked hardware implementations are dedicated to specific algorithms, making them inflexible. On the other hand, applying masking on software involves serious challenges including significant overhead in terms of efficiency and difficulties to maintain theoretical security guarantees in practice. As a result, a line of research has been devoted to enable masked operations in flexible platforms (i.e., microprocessors) by including some masked modules in their hardware, hence a combination of flexibility and performance. In such scenarios, RISC-V is a natural choice as hardware can be adjusted to the extended instruction set. One such attempt presented at CHES 2021 is known as SCARV, which extends the Instruction Set Architecture (ISA) of a RISC-V core with a rich number of first-order masked operations on both Boolean and arithmetic masked operands. In this work, we conduct a comprehensive analysis of SCARV. Instead of relying on empirical measurements to demonstrate security, we utilize tool-assisted evaluations. Through these evaluations, we identified a couple of design flaws that lead to leakage in the masked implementations hosted by the corresponding processor. These flaws are primarily due to the lack of composability of cascaded components. While heuristic and ad-hoc design principles can result in secure, small, and efficient designs, they lack formal security proofs, which may lead to security flaws, like those we report here. Consequently, this work serves as a motivation for using composable masked modules and tool-assisted evaluations when constructing complex circuits.

Benchmarking of quantum fidelity kernels for Gaussian process regression

Abstract Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest. For an unbiased benchmarking of quantum kernels, it is necessary to construct the most optimal functional form of the classical kernels and the most optimal quantum kernels for each given data set. We develop an algorithm that uses an analog of the Bayesian information criterion to optimize the sequence of quantum gates used to estimate quantum kernels for Gaussian process models. The algorithm increases the complexity of the quantum circuits incrementally, while improving the performance of the resulting kernels, and is shown to yield much higher model accuracy with fewer quantum gates than a fixed quantum circuit ansatz. We demonstrate that quantum kernels thus obtained can be used to build accurate models of global potential energy surfaces (PES) for polyatomic molecules. The average interpolation error of the six-dimensional PES obtained with a random distribution of 2000 energy points is 16 cm−1 for H3O+, 15 cm−1 for H2CO and 88 cm−1 for HNO2. We show that a compositional optimization of classical kernels for Gaussian process regression converges to the same errors. This indicates that quantum kernels can achieve the same, though not better, expressivity as classical kernels for regression problems.

Exploring the molecular and neuronal bases involved in central amygdala-dependent control of emotional behaviors

The central extended amygdala, including the central nucleus of the amygdala (CeA) and the lateral division of the bed nucleus of the stria terminalis (BNSTL), is a pivotal brain region involved in the threat processing responsible for emotional states such as fear and anxiety. These brain regions alter their circuit activities and exhibit necessary functions to adapt to environmental changes. When faced with excessive threats or stress, it is thought that these neural circuit functions are disrupted and cause various stress-related psychiatric disorders. The CeA and BNSTL were suggested to be the same nuclei separated during development because of their dense neural connections, and the similarities in cellular composition and connectivity patterns with other brain regions. On the other side, some recent studies suggested functional differences between these two regions in controlling emotional behaviors. However, functional segregation at the subnuclei level was insufficient since the two regions have complex circuit structures composed of multiple subnuclei. In this review, we introduce the similarities and differences between the CeA and BNSTL that have been clarified from our recent comparative studies of gene expression profiles and circuit functions at the subnuclei level. Additionally, we also discuss how it can contribute to understanding the molecular pathogenesis of neuropsychiatric disorders, including stress-related psychiatric disorders.

Foundation model of neural activity predicts response to new stimulus types and anatomy.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

Structural stability hypothesis of dual unitary quantum chaos

Having spectral correlations that, over small enough energy scales, are described by random matrix theory is regarded as the most general defining feature of quantum chaotic systems as it applies in the many-body setting and away from any semiclassical limit. Although this property is extremely difficult to prove analytically for generic many-body systems, a rigorous proof has been achieved for dual-unitary circuits—a special class of local quantum circuits that remain unitary upon swapping space and time. Here we consider the fate of this property when moving from dual-unitary to generic quantum circuits focusing on the , i.e., the Fourier transform of the two-point correlation. We begin with a numerical survey that, in agreement with previous studies, suggests that there exists a finite region in parameter space where dual-unitary physics is stable and spectral correlations are still described by random matrix theory, although up to a maximal quasienergy scale. To explain these findings, we develop a perturbative expansion: it recovers the random matrix theory predictions, provided the terms occurring in perturbation theory obey a relatively simple set of assumptions. We then provide numerical evidence and a heuristic analytical argument supporting these assumptions. Published by the American Physical Society 2024

A Dual Constant Current Output Ports WPT System Based on Integrated Coil Decoupling: Analysis, Design, and Verification

With the high integration of power electronic devices, wireless power transfer (WPT) systems are required to have output characteristics of different specifications that are independent of the load. However, existing methods for realizing dual-output WPT systems have problems such as complex circuits, cumbersome control schemes, low system stability, insufficient system space utilization, and unnecessary cross-coupling. Therefore, in order to solve the above problems, this paper proposes a dual-receiver WPT system with dual constant current (CC) output based on an integrated decoupling coil. In this system, the DD coil is wound vertically in series with the solenoid coil and serves as the first receiving coil to achieve energy transmission in the system. While the solenoid coil is used in the transmitting coil and the second receiving coil, and the coils are perpendicular to each other to achieve natural decoupling. Furthermore, the receiving coils are integrated together on the receiving side ferrite plate. Therefore, there is no cross-coupling interference in the system, which simplifies the system design. Firstly, the natural decoupling characteristics of the magnetic coupler and the coil optimization method are analyzed in detail theoretically. Secondly, a detailed mathematical analysis is performed on the dual CC output characteristics with different specifications that are load-independent and have zero phase angle operation. Again, the zero voltage switching of the inverter can be achieved by changing the compensation component parameters through simulation verification. Finally, a prototype with a rated power of 283 W is constructed for validation purposes. The first receiver delivers a CC output of 3 A, while the second receiver provides a CC output of 4 A, with the DC–DC conversion efficiency reaching a peak of 90.2%. The experimental results confirm the accuracy of the theoretical analysis.

Multiplex Expression Cassette Assembly: A flexible and versatile method for building complex genetic circuits in conventional vectors.

The manipulation of multiple transcription units for simultaneous and coordinated expression is not only key to building complex genetic circuits to accomplish diverse functions in synthetic biology, but is also important in crop breeding for significantly improved productivity and overall performance. However, building constructs with multiple independent transcription units for fine-tuned and coordinated regulation is complicated and time-consuming. Here, we introduce the Multiplex Expression Cassette Assembly (MECA) method, which modifies canonical vectors compatible with Golden Gate Assembly, and then uses them to produce multi-cassette constructs. By embedding the junction syntax in primers that are used to amplify functional elements, MECA is able to make complex constructs using only one intermediate vector and one destination vector via two rounds of one-pot Golden Gate assembly reactions, without the need for dedicated vectors and a coherent library of standardized modules. As a proof-of-concept, we modified eukaryotic and prokaryotic expression vectors to generate constructs for transient expression of green fluorescent protein and β-glucuronidase in Nicotiana benthamiana, genome editing to block monoterpene metabolism in tomato glandular trichomes, production of betanin in tobacco and synthesis of β-carotene in Escherichia coli. Additionally, we engineered the stable production of thymol and carvacrol, bioactive compounds from Lamiaceae family plants, in glandular trichomes of tobacco. These results demonstrate that MECA is a flexible, efficient and versatile method for building complex genetic circuits, which will not only play a critical role in plant synthetic biology, but also facilitate improving agronomic traits and pyramiding traits for the development of next-generation elite crops.

STRAIGHT-IN: a platform for rapidly generating panels of genetically modified human pluripotent stem cell lines.

Targeted integration of large DNA cargoes (>10 kb) or genomic replacements in mammalian cells, such as human pluripotent stem cells (hPS cells), remains challenging. Here we describe a platform termed serine and tyrosine recombinase-assisted integration of genes for high-throughput investigation (STRAIGHT-IN) to circumvent this. First, a landing pad cassette is precisely inserted or used to replace specific genomic regions. The site-specific integrase Bxb1 then enables DNA constructs, including those >50 kb, to be integrated into the genome, while Cre recombinase excises auxiliary DNA sequences to prevent postintegrative silencing. Using a strategy whereby the positive selection marker is only expressed if the donor plasmid carrying the payload is correctly targeted, we can obtain 100% enrichment for cells containing the DNA payload. Procedures for expressing Cre efficiently also mean that a clonal isolation step is no longer essential to derive the required genetically modified hPS cells containing the integrated DNA, potentially reducing clonal variability. Furthermore, STRAIGHT-IN facilitates rapid and multiplexed generation of genetically matched hPS cells when multiple donor plasmids are delivered simultaneously. STRAIGHT-IN has various applications, which include integrating complex genetic circuits for synthetic biology, as well as creating panels of hPS cells lines containing, as necessary, hundreds of disease-linked variants for disease modeling and drug discovery. After establishing the hPS cell line containing the landing pad, the entire procedure, including donor plasmid synthesis, takes 1.5-3 months, depending on whether single or multiple DNA payloads are integrated. This protocol only requires the researcher to be skilled in molecular biology and standard cell culture techniques.