Microwave dielectric ceramics (MWDCs) are pivotal to modern wireless communication systems, with their performance governed by three key parameters: relative dielectric constant (ε_r), Q×f value (product of quality factor Q (reciprocal dielectric loss) and frequency f), and temperature coefficient of resonant frequency (τ_f). This review systematically summarizes the recent research progress of MWDCs from five interrelated aspects. In terms of performance characterization, standardized resonant methods achieve ε_r measurement errors below 1% and a tanδ detection limit as low as 10^-5. Theoretically, frameworks from complex crystal chemistry to the recently elucidated cation rattling effect enable quantitative interpretation of dielectric behavior. In processing, the cold sintering process achieves ceramic densification below 300 °C, reducing energy consumption by over 97% in comparison with conventional sintering. For applications, these materials have been widely deployed in high-performance substrates, resonators, and filters for 5G/6G communications, with device insertion loss maintained below 1 dB. Additionally, data-driven approaches, particularly machine learning, can accurately predict key dielectric properties with a coefficient of determination (R²) higher than 0.9, accelerating the exploration and development of novel MWDCs. By integrating these perspectives, this review offers a systematic insight into the state-of-the-art progress and future development directions of MWDCs research.

Zirconia-toughened alumina (ZTA) ceramics have attracted much attention as electronic package substrate materials because of their excellent mechanical properties and chemical stability. The high sintering temperatures of ZTA ceramics restrict their multilayer co-firing with copper electrodes for low-temperature co-fired ceramic (LTCC) applications. To achieve a balance between good properties and low sintering temperatures, this work proposes CuO–TiO₂–Nb₂O₅ (CTN) composite oxide sintering aids for ZTA ceramics to obtain a novel glass-free LTCC material. A low-temperature densification mechanism based on multiphase synergy and interfacial reactions is revealed. The results show that the sintering temperature of ZTA ceramics doped with 5 wt% CTN can be significantly reduced to 1050 °C, resulting in high thermal conductivity (18.7 W/(m·K)), high bending strength (405 MPa), and low dielectric loss (9.97×10⁻⁴@11.97 GHz). The co-firing compatibility with a multilayer copper inner electrode is also demonstrated. This work overcomes the traditional trade-off between low-temperature sintering and high performance in glass-free LTCC materials and provides a new strategy for the design and development of multilayer ceramic substrates with copper inner electrodes.

The temperature coefficient of resonance frequency (τ_f or TCF) is the key parameter for evaluating temperature stability of microwave dielectric ceramics. In this work, a machine learning framework was proposed to predict the τ_f values of ABO₃-type microwave dielectric ceramics. Leveraging a curated dataset of 104 single-phase ABO₃-type compounds, we systematically evaluated models based on five machine learning algorithms using 31 structural descriptors as input features. The eXtreme Gradient Boosting (XGB) algorithm emerged as the optimal predictive model, demonstrating robust performance on the test set (R² = 0.7799, RMSE = 15.7494 × 10⁻⁶ ℃⁻¹). Consistent results on the validation set further confirmed its generalization capability. Critical features contributing to the model's performance include molecular dielectric polarizability (p_m), tolerance factor (t_t), ionic volume (V_i) and relative molecular mass (m). Structure-property relationship studies revealed that the pm plays an important role in modulating the τ_f value by affecting the permittivity. Quantitative thresholds for these critical descriptors were also derived for identifying materials with near-zero τ_f. This work provides an effective data-driven approach for accelerating the discovery of microwave dielectric ceramics with good temperature stability.

Microwave dielectric ceramics (MWDCs) with a high Q×f value can improve the performance of radio frequency components like resonators, filters, antennas and so on. However, the quantitative structure-property relationship (QSPR) for the Q×f value is complicated and unclear. In this study, machine learning methods were used to explore the QSPR and build up Q×f value prediction model based on a dataset of 164 ABO₄-type MWDCs. We employed five commonly-used algorithms for modeling, and 35 structural features having correlations with Q×f value were used as input. In order to describe structure from both global and local perspectives, three different feature construction methods were compared. The optimal model based on support vector regression with radial basis function kernel shows good performances and generalization capability. The features contained in the optimal model are primitive cell volume, molecular dielectric polarizability and electronegativity with A- and B-site mean method. The relationships between property and structure were discussed. The model used for the Q×f value prediction of tetragonal scheelite shows excellent performances (R² = 0.8115 and RMSE = 8362.73 GHz), but it needs auxiliary features of average bond length, theoretical density and polarizability per unit volume for monoclinic wolframite ceramics to improve model prediction ability.

To date, most of the reported piezoelectric energy harvesters (PEHs) use lead-based Pb(Zr,Ti)O₃ (PZT) piezoceramic family, which is obviously harmful to the environment. In recent years, the PEHs constructed with lead-free piezoceramics have been developed rapidly. However, their force-to-electric (F–E) output performances are still unsatisfactory. To address this issue, here we present a PEH assembled with lead-free potassium sodium niobate (KNN) based co-fired multilayered piezoceramics (MLPCs), which show a high output current and power. First, high-quality KNN-based MLPCs are prepared by tape-casting process. Each MLPC contains 11 piezoceramic layers, and the cross-section SEM image of the MLPC indicates that the ceramic layers are well connected with the Ag/Pd inner electrode layers. The d₃₃ of a single MLPC reaches up to 4675 pC/N. The F–E output performance of KNN-MLPC based PEH is then tested. The inherent advantages of multilayered ceramics enable the PEH to achieve a peak-to-peak output current of up to 1.48 mA and a peak-to-peak output power of 2.19 mW under a harmonic force load of 6 kN at 14 Hz. Finally, the PEH is tested to validate its practical application in real road environments, demonstrating its promising for the use of self-powered monitoring sensors for collecting traffic data.

Local electric-field around multitype pores (dielectric pore, interface pore, electrode pore) in multilayer ceramic capacitors (MLCCs) was investigated using Kelvin probe force microscopy combined with the finite element simulation to understand the effect of pores on the electric reliability of MLCCs. Electric-field is found to be concentrated significantly in the vicinity of these pores and the strength of the local electric-field is 1.5–5.0 times of the nominal strength. Unexpectedly, the concentration degree of the pores in the inner electrode is much higher than that in the dielectrics and dielectric-electrode interfaces. Meanwhile, geometry orientations are found to have a remarkable influence on the local electric field strength. The pores act as an insulation degradation precursor via local electric, thermal center, and oxygen vacancies accumulation center. Such unusual local electric field concentration of multitype pores can provide new insights into the understanding of insulation degradation evolution, processing tailoring and design optimization for MLCCs.

Low permittivity microwave dielectric ceramics (MWDCs) are attracting great interest because of their promising applications in the new era of 5G and IoT. Although theoretical rules and computational methods are of practical use for permittivity prediction, unsatisfactory predictability and universality impede rational design of new high-performance materials. In this work, based on a dataset of 254 single-phase microwave dielectric ceramics (MWDCs), machine learning (ML) methods established a high accuracy model for permittivity prediction and gave insights of quantitative chemistry/structure-property relationships. We employed five commonly-used algorithms, and introduced 32 intrinsic chemical, structural and thermodynamic features which have correlations with permittivity for modeling. Machine learning results help identify the permittivity decisive factors, including polarizability per unit volume, average bond length, and average cell volume per atom. The feature-property relationships were discussed. The optimal model constructed by support vector regression with radial basis function kernel was validated its superior predictability and generalization by verification dataset. Low permittivity material systems were screened from a dataset of ~3300 materials without reported microwave permittivity by high-throughput prediction using optimal model. Several predicted low permittivity ceramics were synthesized, and the experimental results agree well with ML prediction, which confirmed the reliability of the prediction model.