To address the limitations of conventional opamp-free bandgap reference architectures—specifically their high temperature coefficient and insufficient power supply rejection ratio（PSRR）performance，which fail to meet high-precision application requirements—a high-PSRR bandgap reference circuit with segmented curvature compensation was designed. The proposed bandgap reference circuit adopts a voltage self-regulation structure，which suppresses power supply ripple in the low-frequency range through a negative feedback loop and enhances the anti-interference capability of the output voltage. Furthermore，a PTAT²（proportional to absolute temperature squared）compensation circuit was integrated to generate a compensation current，enabling segmented curvature compensation and thus realizing a significant reduction in the temperature coefficient. The proposed bandgap reference circuit was designed based on the SMIC 0.18 μm CMOS process. Simulation results indicate that，at an operating voltage of 3.3V，the bandgap reference output voltage is 1.197V；over the temperature range of-45 ℃ to 125 ℃，the temperature coefficient is 5.38×10^-6/℃. The PSRR of this bandgap reference at low frequencies reaches-103dB，and the circuit has a quiescent current of 14.8 μA.

A bandgap reference voltage source（BGR）adopting a composite compensation method was designed. Based on the Banba-type bandgap reference topology，an additional BJT branch and compensation resistors were utilized to cancel the high-order effects of the base-emitter voltage（V）. A piecewise compensation circuit was also incorporated to achieve curvature compensation over a wide temperature range. Meanwhile，a digital trimming circuit was employed to further reduce the impact of process variations on circuit performance. Designed in the SMIC 180nm BCD process，post-layout simulation results showed that the reference source could stably output a voltage of 800mV with a temperature coefficient of 0.98×10/℃over the temperature range of-40-150℃. When the supply voltage varied from 1V to 3.3V，the output drift was 5.4mV，with a line regulation of less than 0.23%.

To meet the high demands of 500-kW electromagnetic propulsion systems on pulse power supplies in terms of energy density，conversion efficiency，and output stability，this paper presented a pulse power supply design based on a hybrid energy storage system combining supercapacitors and film capacitors. The power supply employed supercapacitor banks for energy storage and film capacitor banks for instantaneous high-power discharge. An efficient discharge topology ensured stable power transmission to the load module，while a three-level protection mechanism enhanced the safety and reliability of the discharge process. Experimental results indicate that the prototype achieves an energy density of 18.3Wh/kg，and a conversion efficiency of 91.74%. The pulse current overshoot is controlled within 5.00%. All these metrics satisfy the technical requirements of 500-kW class electromagnetic propulsion systems. The proposed pulse power supply effectively addresses issues such as low energy density，poor conversion efficiency，and insufficient load adaptability in existing medium-power electromagnetic propulsion pulse power supplies.

To address the temperature gradient requirements for low-temperature soldering and high-temperature service，variable-temperature solders were developed，and the regulatory effects of Ni particle content on their properties and those of brazed joints were clarified. Using 4#SAC305solder powder as the base material，2-5 μm Ni particles and HP01high-activity flux were compounded to prepare Ni-based variable temperature solder paste. The effects of Ni content on the melting characteristics and wettability of the solder paste，as well as the growth of intermetallic compounds（IMCs）at the brazed joint interface and changes in shear strength during aging，were investigated. The results show that when the Ni content is 10%，the solder's melting point can rise to 227 ℃，with only a marginal increase observed after high-temperature storage. Wettability initially enhanced and subsequently decreased as the Ni content increased，resulting in a 60% reduction in the spread area observed at 20% Ni. The growth of the interfacial IMC layer corresponds well with the square root of the aging duration，achieving a thickness of 37.611 μm after 360hours of aging at 20% Ni. Under the same Ni content，shear strength decreases with prolonged aging，and under the same aging condition，it decreases with increasing Ni content. The solder paste with 10% Ni exhibitsminimal impact on shear strength due to aging，and its fracture surface displays a mixed ductile-brittle characteristic. The variable-temperature solder with 10% Ni content demonstrates the best overall performance，effectively raises the solder's melting point while exhibiting excellent stability during high-temperature service，thereby meeting the requirements for temperature gradient applications.

To address the limitations of traditional quality management methods in processing and analyzing massive data for electronic components，this study aims to establish an intelligent quality monitoring mechanism for enhancing the accuracy and reliability of quality assessment. A novel dual-model framework integrating quality monitoring and fault prediction was established using the whole-life-cycle multi-source data: 1）A quality assessment model employing hyperparameter-optimized machine learning algorithms was constructed，utilizing six-dimensional feature data covering factory inspection，in-process quality assurance，and defect records；2）A fault prediction model was designed based on a backpropagation（BP）neural network to enable dynamic early warnings. Experimental validation on JZC-084 electromagnetic relays and J599F26D low-frequency connectors demonstrated that the proposed method achieved a fault prediction error rate lower than 0.1% and a quality assessment accuracy of 95.1%，which exceeded technical specifications. Verification via the random forest classifier showed average precision，recall，and F1-score values of 83.6%，81.2%，and 78.3%，respectively. This data-driven approach significantly enhances scientific decision-making in quality management through real-time monitoring and cross-departmental data synergy. Future work will focus on model parameter optimization and scenario expansion to enhance prediction comprehensiveness.