As the density of electronic packaging continues to rise, traditional soldering techniques encounter significant challenges, leading to copper–copper direct bonding as a new high-density connection method. The high melting point of copper presents difficulties for direct diffusion bonding under standard conditions, thus making low-temperature copper–copper bonding a focal point of research. In this study, we examine the sintering process at various temperatures by constructing models with multiple nanoparticles and sintering them under different conditions. Our findings indicate that 600 K is a crucial temperature for direct copper–copper sintering. Below this threshold, sintering predominantly depends on structural adjustments driven by residual stresses and particle contact. Conversely, at temperatures of 600 K and above, the activation of rapid surface atomic motion enables further structural adjustments between nanoparticles, leading to a marked decrease in porosity. Mechanical testing of the sintered samples corroborated the structural changes at different temperatures, demonstrating that the surface dynamic motion of atoms inherent in low-temperature sintering mechanisms significantly affects the mechanical properties of nanomaterials. These findings have important implications for developing high-performance materials that align with the evolving requirements of modern electronic devices.

To achieve high-frequency inductance and impedance enhancement for effective electromagnetic interference (EMI) mitigation in power electronics, this paper presents an electromagnetic analysis and experimental study of laminated Mn-Zn toroidal ferrite cores. The electromagnetic field is analyzed using a 2D analytical solution based on a simplified Cartesian approximation. Although neglecting curvature, this approach enables efficient eigenfunction expansion and is rigorously validated against cylindrical finite difference (FDM) and 3D finite element (FEM) benchmarks. The results demonstrate that lamination effectively interrupts eddy current loops; notably, a four-layer structure increases the resonant frequency by approximately 2.8 times compared to a monolithic core. Experimental measurements confirm that this design significantly mitigates the skin effect and extends the stable frequency bandwidth. This study establishes a validated, computationally efficient methodology for optimizing core geometries to prevent impedance degradation.

Knowledge distillation achieves model compression by training a lightweight student network to mimic the output distribution of a larger teacher network. However, when the teacher becomes overconfident, its sharply peaked logits break the scale symmetry of supervision and induce high-variance gradients, leading to unstable optimization. Meanwhile, research that focuses only on final-logit alignment often fails to utilize intermediate semantic structure effectively. This causes weak discrimination of student representations, especially under class imbalance. To address these issues, we propose Scale Calibration and Pressure-Driven Knowledge Distillation (SPKD): a one-stage framework comprising two lightweight, complementary mechanisms. First, a dynamic scale calibration module normalizes the teacher’s logits to a consistent magnitude, reducing gradient variance. Secondly, an adaptive pressure-driven mechanism refines student learning by preventing feature collapse and promoting intra-class compactness and inter-class separability. Extensive experiments on CIFAR-100 and ImageNet demonstrate that SPKD achieves superior performance to distillation baselines across various teacher–student combinations. For example, SPKD achieves a score of 74.84% on CIFAR-100 for the homogeneous architecture VGG13-VGG8. Additional evidence from logit norm and gradient variance statistics, as well as representation analyses, proves the fact that SPKD stabilizes optimization while learning more discriminative and well-structured features.

The output quality of random-number sources directly affects the security of cryptographic systems. Physical-noise degradation, environmental disturbance, device aging, and fault injection may increase output bias, correlation, and predictability. This paper presents a resource-conscious on-chip entropy-quality supervisor for random-number source output streams. The design uses non-overlapping 1024-bit measurement windows and a shared feature engine for bit counts, directional transition counts, and run information. These features support repetition-count, adaptive-proportion, and low-toggle checks, together with two-bit pattern-concentration and first-order conditional-transition indicators, exponentially weighted moving-average trend monitoring, comprehensive scoring, and a seven-bit alarm bitmap. The RTL accepts a 32-bit valid-data interface, makes one decision every 32 valid words, and is integrated into an Artix-7 XC7A35T project configured with a 50 MHz system-clock constraint. The complete project includes a ring-oscillator TRNG, and controlled deterministic fault patterns are inserted into selected windows of the TRNG stream for fault-response verification. Deterministic RTL simulations show complete alarm mappings of 0111111 for fixed-value patterns, 0011000 for an isolated alternating window, and 1011000 for the fourth consecutive alternating window. A parameterized capture simulation also verifies pre-event, injection, and recovery sequencing. FPGA implementation results show that the entropy-supervisor core uses 1009 LUTs and 276 flip-flops without BRAM or DSP resources, while the complete project uses 1313 LUTs, 614 flip-flops, and one BRAM tile. The design meets the 50 MHz clock constraint with a WNS of 1.079 ns and no setup or hold violations. The total on-chip power reported by Vivado is 0.076 W; without simulation-derived switching activity, this value is approximate and is not a board measurement. The results establish the functional behavior of the monitoring and decision paths. The two-bit pattern-concentration and first-order conditional-transition indicators are empirical tools for online anomaly diagnosis; neither is a min-entropy estimator, and they do not replace source-specific entropy assessment under NIST SP 800-90B.

The foundation of modern vehicle control relies on Electronic Control Units (ECUs) communicating via the Controller Area Network (CAN). However, CAN was not designed with security in mind. Limited bandwidth and lack of security make CAN vulnerable, while centralized solutions like AUTOSAR SecOC suffer from high latency. To solve this problem, we propose a novel security protocol named Self-Updating Hash Chain Layered Security Protocol (SUHC-LSP), which uses a “space-time coupled” frame structure to fit a robust authentication code into the limited CAN data field. Unlike conventional schemes that require explicit freshness negotiation during routine operation, SUHC-LSP adopts a self-updating hash-chain mechanism in which chain evolution proceeds autonomously under normal conditions. In addition, SUHC-LSP introduces a self-updating hash chain mechanism that enables freshness iteration during steady-state operation. Traditional AUTOSAR SecOC configuration schemes require additional synchronization messages and freshness counter management, whereas the approach proposed in this paper eliminates the resulting bandwidth overhead while adhering to the 8-byte CAN payload limit and ensuring message authenticity and integrity. In addition, a risk-adaptive two-layer architecture is designed to balance fast speed for local messages and strong encryption for cross-domain messages. BAN-logic and ProVerif verification show that the protocol preserves authentication, freshness, secrecy, and event-correspondence properties under the stated assumptions. Experiments on an STM32 platform show that, in a 4-device prototype, the computational overheads are 1.61 ms for intra-domain communication and 0.83 ms for inter-domain communication. Moreover, analytical overhead comparison indicates that the proposed protocol has lower sensitivity to network scale than the compared schemes.