Search Results (480)

With the increasing demand for miniaturization, multi-channel RF transceiver capability, and high-speed digital processing in unmanned aerial vehicles, satellite remote sensing, and anti-jamming communication systems, conventional board-level discrete integration schemes face significant limitations in terms of interconnect length, parasitic effects, volume and weight, and electromagnetic compatibility. This paper proposes a high-density integrated RF microsystem packaging scheme based on a 12-layer ABF organic substrate. Within a package size of 37.5 mm × 37.5 mm, the microsystem integrates a digital processing chip, two DDR3 memories, two Flash memories, two broadband RF transceiver chips, and passive components, thereby realizing a 4-receiver/4-transmitter MIMO architecture. Compared with a conventional discrete PCB-based integration scheme, the proposed microsystem reduces the board-level occupied area by approximately 70% and decreases the weight by more than 30%. To address the non-reworkable nature of the in-package DDR3 address/command/clock links and their sensitivity to high-speed signal integrity, a field-circuit co-simulation method combining three-dimensional electromagnetic S-parameter extraction with IBIS models is adopted to optimize the transmission-line impedance and termination parameters under a fly-by topology. The results show that by optimizing the DDR3 clock-line impedance from the conventional 100 Ω differential impedance to 80 Ω differential impedance, and the address/control-line impedance from the conventional 50 Ω single-ended impedance to 40 Ω single-ended impedance, together with 40 Ω and 120 Ω terminations, respectively, overshoot and ringing can be effectively suppressed. The DDR3 eye width is improved from 0.89 ns to 0.92 ns. To address impedance discontinuities in the vertical interconnects of RF channels, a refined structure combining enlarged antipads and accompanying ground vias is proposed. As a result, the worst-case return loss of the RF channel is improved from below 16 dB to 19.69 dB, the maximum insertion loss is reduced from above 0.5 dB to 0.35 dB, and the worst-case inter-channel isolation is improved from below 60 dB to 73.42 dB. To mitigate thermal coupling and localized thermal isolation caused by thickness differences among multiple chips, a locally recessed copper heat spreader is designed, reducing the junction-to-case thermal resistance of the RF chip from 1.25 °C/W to 0.50 °C/W, corresponding to a reduction of approximately 60%. Preliminary hardware-in-the-loop frequency-hopping tests based on the proposed microsystem demonstrate that the system can achieve an analog frequency-hopping rate exceeding 4000 hops/s and a hybrid frequency-hopping rate exceeding 10,000 hops/s. The results indicate that the proposed packaging scheme provides a feasible engineering implementation path for high-density, multi-channel RF microsystem design. Full article

Resistive random-access memory (RRAM) has emerged as one of the most promising device platforms for neuromorphic, in-memory computing because its two-terminal metal–insulator–metal (MIM) structure can reproduce the weight-update behavior of biological synapses while remaining compatible with mainstream CMOS processing. This review summarizes the current state of RRAM-based neuromorphic technology from four complementary perspectives: device physics, materials, fabrication processes, and packaging. We first describe the operating principles of filamentary and interface-type RRAM, including the forming/set/reset switching sequence, and the two dominant analytical frameworks used to describe the reset transition—the ion-migration model and the thermally driven filament-dissolution model. We then review the switching-layer and electrode materials that have been most widely investigated such as HfO_x, TiO_x, TaO_x, ZnO, ZrO₂, and Cu/Ag-based conductive-bridge systems, together with representative bilayer and doped architectures reported for synaptic devices. The biological functions that RRAM can emulate are discussed alongside the non-ideal characteristics that currently limit on-chip training accuracy, with emphasis on separating device-to-device from cycle-to-cycle variability and on the workload-dependent nature of endurance and retention requirements. We further summarize the process technologies used to integrate RRAM into large-scale, CMOS-compatible arrays, including atomic layer deposition, interfacial oxygen-reservoir engineering, low-thermal-budget back-end-of-line integration, and three-dimensional vertical RRAM patterning, and discuss the advanced packaging strategies such as 2.5D/3D heterogeneous integration, chiplet architectures, thermal-interface materials, and nanostructured underfills required to manage the power density and interconnect demands of large synaptic arrays, distinguishing solutions that have been demonstrated specifically for RRAM neuromorphic arrays from those that remain general advanced-packaging concepts. Finally, RRAM is benchmarked against competing emerging non-volatile memories, and the key research directions such as three-terminal memtransistor architectures, three-dimensional integration with high-performance selectors, and hardware–algorithm co-design that will determine whether RRAM-based neuromorphic hardware can move from laboratory demonstrations to on-device AI, autonomous systems, and large-scale artificial-neural-network accelerators are outlined. Relative to prior reviews that focus primarily on RRAM device physics or on switching-layer materials in isolation, the distinctive contribution of this review is an explicit, cross-layer synthesis that connects device-level non-idealities to their consequences for wafer-scale process integration and for advanced 2.5D/3D packaging—a combination that, to our knowledge, has not been jointly treated in the recent review literature on RRAM-based neuromorphic hardware. Full article

Enhancing the proof mass without enlarging the chip area or introducing structural asymmetry is a central challenge in the development of low-noise microelectromechanical system (MEMS) accelerometers. Here, we present a symmetric triple-layer MOEMS accelerometer and analyze its device-level sensitivity trade-off, cross-axis coupling behavior, and dynamic consistency between measurement and finite-element simulations. The proposed sensing element sandwiches one without-beam mass layer between two identical with-beam layers, thereby increasing the effective proof mass while preserving mirror symmetry. A lumped-parameter model is developed to explain the sensitivity trade-off among single-layer, asymmetric double-layer, and symmetric triple-layer configurations. Finite-element simulations are used to distinguish translational cross-axis coupling from rotational cross-axis coupling. The experimental characterization of one packaged triple-layer prototype demonstrates a mechanical sensitivity of 193.91 µm/(m/s²), a 10 min output RMS fluctuation of 1.81 µg, and a measured first-order resonant frequency of 11.23 Hz, in close agreement with the tolerance-included finite-element prediction of 11.40 Hz. The resonance bandwidth further yields an apparent package-level quality factor of approximately 374 under ambient pressure, providing additional characterization of the packaged device dynamics. Full article

To address the challenge of balancing junction temperature prediction accuracy and computational efficiency for high-power multi-chip IGBT modules in automotive applications during complex electro-thermal conversion processes, this study proposes an improved three-dimensional thermal network model based on equivalent power loss injection. Firstly, the effective heat conduction area of each packaging layer under actual heat flow distribution is extracted through three-dimensional finite element simulation, and the single-chip self-heating network parameters are constructed. Secondly, targeting the thermal cross-coupling effect among multiple chips, an elliptical thermal diffusion model is applied to accurately define the thermal coupling region, and a dynamic equivalent power loss compensation mechanism is introduced. Efficient decoupling of multi-heat-source interference is achieved without increasing the state-space dimension of the model. An experimental benchmarking results comparison indicates that the absolute error of junction temperature prediction by this model under steady-state operating conditions is 0.5 °C. Further comparative analysis under the full CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car) cycle verifies that the improved model not only overcomes the shortcomings of the traditional Foster model, which severely underestimates the transient peak junction temperature and alternating stress amplitude, but also effectively filters out non-physical overshoots caused by short-term ultra-narrow pulses, thus reasonably estimating the device’s maximum junction temperature within the real physical boundary. This method provides efficient theoretical support for accurate dynamic junction temperature predictions and reliability evaluations of electric vehicles under complex operating conditions. Full article

To investigate the influence of substrate curvature and process deviations on the bonding quality of microchips mounted on curved surfaces, a thermo–mechanical coupled finite element model was developed in ANSYS 19.2 using a 0402 microchip as a representative component. The model was employed to evaluate the effects of substrate curvature radius, chip placement position, chip angular misalignment, and thermocompression-head angular deviation on the stress distribution and bonding behavior during conformal assembly. The simulation results were further validated through thermocompression bonding experiments. The results show that decreasing the substrate curvature radius significantly increases the stress concentration in the chip-pad region and leads to a more non-uniform stress distribution. In addition, placement errors and loading-direction deviations adversely affect bonding quality by altering the contact stress distribution and increasing the tendency for chip displacement and sliding. Quantitative analysis reveals the relative sensitivity of bonding performance to different geometric and process parameters, providing insight into the dominant thermo–mechanical mechanisms governing curved-surface assembly. Experimental results further demonstrate that excessive thermocompression tilt angles can significantly reduce bonding strength and increase chip sliding, suggesting that the tilt angle should be controlled within an appropriate range to ensure assembly reliability. The proposed thermo–mechanical modeling approach provides a quantitative tool for evaluating the influence of process variations on curved-surface microchip assembly and offers practical guidance for process parameter selection, tolerance control, and reliability-oriented design of conformal electronic packaging. Full article

Field-programmable gate arrays (FPGAs) with on-package high-bandwidth memory (HBM) are an attractive substrate for low-latency, precision-customizable AI inference, yet high-level synthesis (HLS) flows expose little control over how tensors are distributed across many independent memory channels. The AMD Alveo V80 spreads its nominal 820 GB/s across 64 pseudo-channels, each capped at 12.8 GB/s, so delivered bandwidth is governed by an explicit tensor-to-channel partitioning decision that existing framework-based flows do not surface. This paper presents a methodology for HLS-based inference on the V80 built on two coupled contributions: a design-time partitioning framework that assigns each tensor to on-chip (BRAM/URAM) storage, a single HBM channel, or a stripe across several channels according to its reuse and access pattern, and a three-stage HLS flow that separates functional baseline, unroll-and-partition throughput extraction, and precision-aware DSP packing. The methodology is developed through five model kernels—a parameterized GEMM, ResNet-18, ViT-Small, a BERT attention block, and GPT-2 Small—and realized as nine engines spanning GEMM (FP32, INT8, INT4), convolution, attention, layer normalization, SoftMax, pooling, and embedding lookup. All nine engines are synthesized in Vitis HLS 2024.2 and placed, routed, and executed on the physical Alveo V80 at 400 MHz (2.5 ns period), and every engine closes timing with positive worst-case slack. We report per-engine cycle counts, post-route utilization, and post-route dynamic-power estimates, and compare engine latency and energy against NVIDIA Titan RTX (GPU) and Intel Xeon W-3223 (CPU) baselines on identical kernels. A central result, confirmed on silicon, is that INT8 roughly halves the GEMM DSP58 footprint relative to FP32 (27 to 14 slices), whereas INT4 yields no further compute reduction and acts purely as a memory-placement lever. The full-model compositions, the roofline classification, and the tensor-to-channel placement framework are analytical, design-time results rather than end-to-end measured performance. The bandwidth-scaling premise underlying the placement framework is confirmed directly on the V80: a 1-to-64 channel sweep shows aggregate HBM bandwidth scaling near-linearly to within 5–13% of the device peak, and placing the GPT-2 LM-head operand on a single channel versus the eight the framework assigns it yields a measured 6.81× speedup—a direct on-board test of the striping decision. The resulting design guidelines target HLS practitioners working with HBM-equipped FPGAs. Full article

The power density of modern high-power integrated processors keeps rising rapidly. Among them, chiplet-based high-power AI accelerators exhibit local peak heat flux exceeding 1 kW/cm², which leads to concentrated hotspots, severe internal temperature gradients, device performance degradation and reliability deterioration. Conventional heat dissipation approaches are limited by the bottleneck of series interfacial thermal resistance and fail to meet the cooling demands of complex integrated architectures. Chip-level thermal management serves as a core method to suppress hotspots near heat sources and reduce overall system thermal resistance, which guarantees long-term stable operation of high-power integrated processors and plays a vital role in improving the energy efficiency and service life of computing platforms. This paper systematically reviews mainstream chip-level thermal management technologies for high-power integrated processors, covering heterogeneous integration of high-thermal-conductivity substrates, embedded microchannel liquid cooling, solid-state active heat pumps, multi-physics co-design and advanced packaging manufacturing processes. The basic working principles and state-of-the-art research progress of each cooling technology are elaborated in detail. The common engineering bottlenecks, including ultra-high heat flux endurance, packaging process compatibility, fluid leakage risks and multi-layer interfacial thermal resistance, are summarized, and the future development trends of this field are clarified. This review can provide comprehensive theoretical guidance for structural design and large-scale engineering implementation of near-junction thermal management solutions for various high-power integrated processors, especially high-computing-power AI accelerators. Full article

Field-Programmable Gate Array System-on-Chip (FPGA-SoC) platforms are increasingly adopted in modern industrial Cyber-Physical Systems (CPSs), enabling real-time control, monitoring, and automation of critical industrial processes. The increasing integration density of modern FPGA-SoC architectures introduces new thermal security challenges, where heat evolves from a reliability concern into a potential source of information leakage. Thermal Side-Channel Attacks (TSCAs) exploit runtime thermal variations to infer sensitive operational, architectural, or cryptographic information from the underlying hardware. While this study is centered on FPGA-SoC platforms, comparable thermal security challenges are increasingly reported across other densely integrated computing architectures, including Multiprocessor System-on-Chip (MPSoC), System-in-Package (SiP), and emerging Three-Dimensional Integrated Circuit (3D-IC) technologies. Consequently, the detection of thermal side-channel intrusions has become a critical hardware security challenge for next generation industrial CPS infrastructures. To address this challenge, an AI-enabled Digital Twin (DT) framework is introduced for TSCA detection in densely integrated FPGA-SoC microarchitectures. By combining thermal behavioral modeling, feature engineering, and machine learning-based anomaly detection, the proposed framework extends conventional Thermal Digital Twin (TDT) approaches beyond monitoring and mitigation toward autonomous thermal threat detection. The proposed framework is experimentally validated using an NI myRIO-1900 platform integrating a Xilinx Zynq-7010 FPGA-SoC representative of modern industrial embedded control architectures. Experimental results demonstrate the feasibility of the proposed framework, achieving an accuracy of approximately 75% with an Area Under the ROC Curve (AUC) of 0.76 using a lightweight Isolation Forest model. These results validate the capability of the proposed AI-enabled Digital Twin framework to learn normal thermal behavioral patterns and autonomously detect anomalous thermal activities potentially related to TSCAs. Full article

The parameter degradation and failure mechanisms of 1200 V asymmetric trench-type (AT) silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) under various single and repetitive surge currents, with various gate bias voltages (V_GS) of 0 V, −5 V, and −10 V, are systematically investigated in this work. It is indicated that V_GS has no impact on the single surge reliability, with the same maximum single surge current (SSC_max) under different V_GS. However, during repetitive surge stress (90% and 60% SSC_max), the maximum surge cycles have increased as V_GS increases from −10 V to 0 V. It may be caused by the enhancement of channel-assisted leakage conduction, allowing more surge current to flow through the channel. It is concluded from gate capacitance (Cg-Vg) and low-frequency noise (LFN) characterizations that lower V_GS increases SiC/SiO₂ interface defect density, accelerating parameter degradation during single and repetitive surge stress. Both chip and package failures are observed for single and repetitive surge stress. For single surge stress, the device failure has resulted from the melted source Al as the metal erodes and penetrates through the interlayer dielectric and the ohmic contact layer between the source metal and the SiC-doped region, respectively, leading to a three-terminal short circuit. For repetitive surge stress, the device failure has been caused by the penetration of Al metal into the interlayer dielectric, leading to a gate-source short circuit. This comprehensive research provides valuable guidance for enhancing the surge reliability of SiC MOSFETs. Full article

DRAM-based in-memory computing integrates computational regions into the main memory, enabling local data processing within the memory, thereby achieving faster and more efficient data computation. However, enhancing system performance requires addressing a critical challenge: achieving more general and sufficiently powerful data processing capabilities within DRAM-PIM. Existing DRAM-PIM implementations often suffer from limited computational capabilities due to the shared standard DRAM package area between memory cells and computational circuits or because the operator circuits are overly customized, which limits their ability to meet required data processing demands. To address this issue, in this paper, we propose a Super Memory Processing Unit (SMPU). The SMPU uses Hybrid Bonding technology to 3D-stack DRAM and many-core computational clusters, enabling large-bandwidth (0.25 TB/s per-bank, 2 TB/s for 8-bank system bandwidth) on-chip data transmission between DRAM and the computational cluster via copper interconnects, effectively breaking the memory wall bottleneck of existing computing architectures. The SMPU constructs a dual-channel fine-grained computational cluster at the logical computing layer, providing flexible and ample computility for various AI models, such as ResNet50 and Llama2. The SMPU uses standard DDR protocols and integrates a new memory space allocation and parsing controller to ensure system compatibility without modifying the host-end hardware, facilitating the integration and invocation of computility in memory particles. Additionally, the SMPU features an independent dual-channel memory-management mechanism within the memory particles, enabling simultaneous multi-channel, multi-modal AI model inference. We compared a CPU system equipped with an SMPU to current computing systems using FPGA simulations. The FPGA simulation results show that, under the same computational configuration, the system with the SMPU improves the performance of ResNet50-v1.5 by up to 5.1× and Llama by up to 27.43× compared to the base system, while reducing system power consumption by 71.6% (ResNet50-v1.5) to 77.8% (Llama 7B). Full article

The exponential growth of AI-driven data traffic necessitates the evolution of Data Center Networks toward high bandwidths and sub-microsecond latency. While co-packaged optics (CPO) offer a pathway to reduced energy consumption and increased capacity, they introduce significant challenges in optical chip coupling and packaging complexity. This study explores monolithically integrated metasurfaces as an alternative for optical interfaces, potentially reducing the need for bulky external microlens arrays or extremely precise mechanical alignment. We design an amorphous silicon (a-Si) metasurface on a Silicon-On-Insulator (SOI) platform operating at 1310 nm. By spatially mapping nanopillar radii to satisfy a spherical phase profile, we achieved near-vertical beam emission with an emission angle of 0.88° focused at a focal length of 98.99 μm. Broadband characterization across a 20 nm band confirms stable focusing and a confined spot size with moderate roll-off toward the band edges. The sensitivity of the emission profile of the device to fabrication imperfections in pillar radius, height, and sidewall taper is quantified. The coupling to a polymer-based optical redistribution layer (ORDL) is also studied, and the corresponding modal analysis demonstrates a maximum coupling efficiency of 68.2% into an SU-8 polymer waveguide. Tolerance analysis results reveal deterioration of 0.9 dB and 0.4 dB for $\pm 0.6 \mathsf{\mu }\mathrm{m}$ horizontal and $\pm 1.5 \mathsf{\mu }\mathrm{m}$ vertical misalignment respectively, making the interface compatible with relaxed alignment assembly assumptions, although experimental packaging validation remains required. The methodology is further validated at 1550 nm, demonstrating its applicability across telecom bands. These results suggest that integrated metasurfaces may simplify the packaging stack and enhance density for next-generation CPO links by providing precise, on-chip wavefront manipulation. Full article

This article introduces the current status of the 0.18-micron CMOS MEMS foundry service platform provided by the Taiwan Semiconductor Research Institute (TSRI), extensively covering the CMOS MEMS components that it has supported in development and fabrication. It also attempts to expand the foundry service scope to the broader categories of capacitive sensors and electrostatic actuators. On the one hand, for fabless MEMS component designers, TSRI currently directly allows the design of two types of components: flow sensors with uniformly perforated membranes and actuators with comb-shaped interdigital electrodes. This service also includes tape-out and wire bonding packaging procedures, following procedures similar to those used by general IC designers. On the other hand, this article specifically presents a clear and feasible approach for MEMS designers equipped with simple wet-etching facilities and a clear and feasible approach to develop further CMOS MEMS components such as capacitive pressure sensors, accelerometers, micro mirrors, and scratch drive actuators with minimal post-processing and chip packaging steps. This work provides a practical CMOS-MEMS design and post-processing guideline for extending the current TSRI foundry platform toward capacitive sensing and electrostatic actuation applications with minimal additional fabrication complexity. Full article

The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, memory hierarchy, packaging, timing, and design automation. Rather than converging on a single hardware solution, the field is expanding into a heterogeneous ecosystem that includes data-center graphics processing units (GPUs), edge neural processing units (NPUs), and application-specific integrated circuits (ASICs), field-programmable gate array (FPGA)-based and hybrid AI system-on-chip (SoC) platforms, chiplet-enabled systems, and emerging beyond-conventional-silicon approaches such as photonic, neuromorphic, and analog in-memory processors. This paper presents a comprehensive review of AI-on-chip systems from a cross-layer perspective. It examines AI chip architectures and hardware platforms, network-on-chip (NoC) designs for AI communication patterns, and algorithm–hardware co-design methods for model acceleration, including compression, quantization, and sparsity-aware optimization. It also reviews clocking, synchronization, and clock-domain-crossing (CDC) challenges in large heterogeneous systems and chiplets, as well as manufacturing, advanced packaging, and reliability issues, including two-and-a-half-dimensional (2.5D) and three-dimensional (3D) integration, thermal and mechanical constraints, assembly quality, and long-term yield considerations. In parallel, the paper surveys the growing role of AI in chip design itself, covering machine-learning-assisted analysis, Bayesian and reinforcement-learning-based optimization, and the emerging use of large language models (LLMs) and AI agents for register-transfer level (RTL) generation, design-space exploration, and autonomous electronic design automation (EDA) workflows. Finally, it discusses beyond-silicon AI chip directions and the broader economic and industry context shaping cloud, on-premises, and edge deployment. By integrating these topics into a unified framework, this review highlights the key technological drivers, system-level tradeoffs, and future research directions that will define next-generation scalable, reliable, and energy-efficient AI-on-chip systems. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

The high-temperature operating requirements and the issues in the packaging process of wide-bandgap power semiconductors have positioned pressureless sinter-bonding using Ag nanoparticle paste as the most promising die-attach technology. However, under pressureless conditions, where externally applied pressure-driven particle rearrangement is absent, achieving sufficient densification and suppressing residual porosity during short-duration annealing at 250 °C remain significant challenges for conventional single-composition Ag pastes. In this study, a hybrid filler paste composed of Ag nanoparticles and a Ag complex solution was developed to implement an active mass supply strategy, in which additional Ag atoms were directly introduced into interparticle voids through in situ reduction during sinter-bonding. Mono-dispersed Ag nanoparticles with a mean diameter of 75.26 nm were synthesized via H₂O₂-mediated wet-chemical reduction, and the Ag complex solution was prepared using a Ag salt–complexing agent–formic acid system dispersed in an ethylene glycol medium. TG-DTA analysis of the hybrid paste revealed four sequential thermal stages, consisting of solvent evaporation, Ag ion reduction, organic decomposition, and interparticle sintering, accompanied by approximately 16 wt% out-gassing. Based on these results, a three-step temperature profile was designed to initiate sintering after complete out-gassing. When chip/paste/substrate assemblies, pre-dried at 50 °C for 90 s and pre-compressed at 5 MPa for 60 s, were subjected to the three-step profile with a peak temperature of 250 °C, the in situ reduced Ag effectively bridged adjacent nanoparticles and filled fine interparticle voids, leading to pronounced densification of the bond line. As a result, the hybrid paste achieved an average shear strength of 19.1 MPa, exceeding the minimum requirement for sinter-bonding applications. These findings demonstrate that the proposed hybrid filler approach provides an effective pathway for enhancing pressureless Ag sinter-bonding performance. Full article