The fight-or-flight response (FFR) is an instantaneous organismic response driven by emotions to external stimuli. However, most current intelligent systems requiring real-time environmental response overlooked this fundamental mechanism. Meanwhile, endowing systems with pre-response adaptive decision-making ability based on environment complexity (EC) considerations also necessitates in-depth research. This work proposes a decision-making model with a bionic memristive circuit comprising n FFR circuits and one environment enrichment (EE) module. The FFR circuit simulates FFR and implements long-term emotion memory (LTEM), memory forgetting, emotion generalization and fast emotion arousal. The EE module allows the circuit to dynamically adjust response targets in multi-stimulus scenarios by considering EC. Consequently, the circuit achieves adaptive and minimal delay responses to changeable stimuli via pre-response decision-making, validated by PSPICE simulations. The use of memristors facilitates online in-situ operating and in-memory computing, which makes the circuit expected to be deployed on multi-nozzle fire-fighting robots, enabling them to adaptively prioritize target processing in real time based on the urgency of various targets in multi-fire points scenarios.

The state of the art analog circuit design applies mainly a full-custom layout methodology. This demands high expertise and heavy manual workload. Additionally, neither can the resulting layout be re-used easily across different designs or different PDKs. Learning from digital standard cells, existing work has proposed stem cells that are abuttable. But stem cells have a fixed area ratio of 2 over same-sized Pcells, limiting its wide application. In this paper we develop a new type of abuttable analog cells (called Acells) for transistors and passive elements. Acells are compatible with digital standard cells and can be abutted in all directions, enabling the use of automatic digital place and route (PnR) engines. We automate Acell generation and show that the average area ratio over same-sized Pcell is 1.49 for 65nm technology and 1.3 for 28nm technology, and is expected to decrease for more advanced technologies. We then use digital PnR to automatically layout a number of analog and mixed-signal (AMS) circuits mainly in 28nm, and show that compared to Pcell-based manual layout, Acell-based layout obtains similar performance and its circuit level layout area is about 2% higher for large scale AMS circuits in our experiments.

This work proposes a bio-inspired decision-making memristive circuit drawing on Hull’s secondary learning system. This circuit can not only mimic the decision-making initiated by secondary drive stimuli and shaped and guided by secondary reinforcers via integrating classical conditioning (CC) and operant conditioning (OC), but also consider the factors that influence decision-making, such as demand states, incentive motivation, and habit strength. These bionic functions have not yet been implemented by existing memristive circuits. Our circuit primarily includes CC module, drive regulation module, habit memory module, incentive generation module, and winner-takes-all module, which is designed through a modular hierarchical circuit design method. Memristors play a core role in our circuit and enable the circuit to perform brain-like online learning in an in-memory computing way, which has power and area advantages. The PSPICE-based simulations in various scenarios show that our circuit has a strong adaptive decision-making ability since more bionic features are considered. The proposed circuit can be applied to a bionic intelligent robot, enabling the robot capable of autonomous associative learning abilities to perform complex tasks such as detection and rescue.Note to Practitioners—This work is motivated by the problem of neuromorphic circuit design for bio-inspired learning and decision-making. To realize brain-like online in-situ learning in an in-memory computing way and enhance the adaptability of the circuit in dynamic environment, a memristive circuit integrating CC and OC is proposed. Referring to Hull’s secondary learning system, the proposed circuit takes into account factors that affect decision-making, such as demand states, incentive motivation, and habit strength, as well as the fact that decision-making processes can be evoked by secondary drive stimuli and shaped by secondary reinforcers, which is unaddressed by existing memristor-based works. The bionic foraging simulations in various scenarios show that our circuit can make favorable adaptive decisions based on various cues categorized by associative memories when engaging with their environment. Such a bio-inspired memristive circuit system can be applied to bionic robots or rescue detection robots through large-scale integration to achieve adaptive learning and decision-making of complex tasks with low power consumption.

Device models require large numbers of parameters to characterize complex physical effects. Although the latest advancements in machine learning and automated tools have drastically improved efficiency over the classic methods, they still demand a considerable amount of human intervention in the loop to gain accuracy. This drastically limits further automation. Inspired by the success of Multimodal Large Language Models (MLLMs) in addressing tasks across diverse fields, we propose ModelGen, the first in-depth study to leverage MLLMs with RAG (Retrieval-Augmented Generation) to significantly reduce human effort in parameter extraction for compact model. Our contributions include (1) Automated Agentic Workflow Construction that learns to build and refine extraction workflows through iterative optimization, (2) MLLM Judge, a visual scoring mechanism that evaluates fitting quality using actual device characteristic plots rather than simple numerical metrics, and (3) Model-specific RAG for providing relevant domain knowledge during the extraction process. Experimental results demonstrate that ModelGen achieves a 26.8%–33.1% improvement in pass@1,3,5 compared to base LLM methods. The system completes complex model extractions for BSIMs and ASM-HEMT in hours (up to 168× faster) rather than days or weeks, making parameter extraction more accessible to non-experts while maintaining professional engineer-level accuracy.

Reservoir computing (RC) contains two significant variants: wide RC and deep RC. The hybrid wide-deep architecture absorbs their strengths with a powerful parallel processing capability while enhancing the memory capacity of reservoirs. However, the fully analog RC circuit combining the two structures has yet to be proposed, mainly due to unmanageable hierarchical signal processing. Here we report a full-analog memristive RC circuit with a hybrid wide-deep architecture comprising an input module, mask module, reservoir module, and readout module. The input module can generate continuous voltages with temporal sequences. The mask module provides parallel mask processes, laying the foundation for implementing a wide RC structure. The reservoir module includes dynamic memristors and postprocessing circuits. Dynamic memristors can produce high-dimensional reservoir states, and postprocessing circuits allow memristive reservoir circuits to be cascaded to achieve a deep RC structure. The readout module mainly consists of a nonvolatile memristor crossbar and an analog integrator, enabling an efficient multiplication-and-accumulation operation. The simulation results in LTspice illustrate that the memory capacity of the proposed circuit is 91.6% higher than that of wide RC. Moreover, it can efficiently perform temporal tasks, obtaining a high accuracy of 98.99% in arrhythmia detection.

In this contribution, the problem of fixed-time stabilization in multi-weighted complex networks via the novel adaptive pinning nonchattering control based on the linear matrix inequality (LMI) method, as well as its application to image protection is addressed. Different from the traditional methods, a novel fixed-time stable form is proposed and the convergence time is estimated based on beta function. Next, utilizing the designed continuous adaptive control strategy, a sufficient LMI condition is presented to ensure the fixed-time stabilization of multi-weighted complex networks. Furthermore, the novel nonchattering adaptive pinning control protocol is given to guarantee the fixed-time stabilization of the system only by controlling a small number of nodes. Note that a scheme of how to select the number of control nodes is put forward accordingly. Finally, the effectiveness of the proposed method is verified by the actual financial model. Meanwhile, a number of encryption experiments are carried out based on three networks, and the mean and variance of the encryption performance are calculated to show the stability and robustness of image encryption different from the existing research works.