Internships

Primarily responsible for code development in the panel design automation project, focusing on secondary development based on Huada EDA tool API to build an automated placement and routing system for PxE/GOA units. During development, not only needed to implement basic automation logic, but also conduct constraint rule modeling for panels of different sizes (55 to 98 inches) to ensure the code has large-scale adaptability. Meanwhile, participated in cutting-edge research on AI technologies in the EDA field, integrated algorithm concepts such as reinforcement learning into the code path planning module to optimize overall execution efficiency. In addition, responsible for organizing project technical achievements and participating in the writing of relevant academic papers to transform engineering practice experience into theoretical achievements.

Significant engineering achievements were made during this internship. The developed automated routing system achieved 100% routing success rate and DRC compliance rate, effectively solving the problems of low efficiency and high error rate in manual routing. In response to the adaptation needs of multi-size panels, the code can quickly adapt to different specifications from 55 to 98 inches after optimization, and the space utilization rate has also been significantly improved. The automation tool developed based on Huada Tool API has been successfully put into use in actual projects, improving the overall design efficiency by more than 30% compared with traditional manual methods. In addition, the technical paper participated in writing has completed the first draft, laying a solid foundation for subsequent academic publication.

Technical Skills: Mastered advanced Python programming skills, especially accumulated rich experience in API development and automation script writing. Familiar with the secondary development process of EDA tools, understood the professional field knowledge of panel design, and able to combine programming technology with actual industry needs. At the same time, gained a deeper understanding of the application of AI algorithms in the engineering field, and possessed the ability to transform theoretical algorithms into actual code.

Soft Skills: Improved technical research and paper writing abilities, learned how to transform engineering practice into academic achievements. Cultivated engineering thinking during project development, focusing on code maintainability and performance optimization. Enhanced communication and coordination abilities through cross-team collaboration, able to accurately understand business requirements and transform them into technical solutions, while improving the ability to analyze problems and independently solve complex engineering problems.

As a hardware testing intern, primarily responsible for the full lifecycle reliability testing of Bosch's new generation BM4120 MEMS inertial sensors. In terms of chip die bonding process optimization, formulated and executed precise test protocols, established standardized test operation guidelines, and reconstructed the resource reservation mechanism of the Demand Request system to improve the utilization efficiency of test resources. At the data processing level, built an automated data Pipeline based on Python to realize cleaning, alignment and integration of multi-source test data, greatly improving data processing efficiency. In addition, responsible for the design, supplier communication and procurement of high-precision test fixtures, debugging and optimization of the actual measurement mechanism of Dummy Phone simulators, and building a new generation of test benches to ensure the accuracy and reliability of test data.

Multiple substantive achievements were made during the internship. Through the construction of Python data Pipeline, the processing efficiency of multi-source test data was improved by 70%, realizing automated cleaning and integration of test data. The established standardized test guidelines standardized the test process, and the optimized resource reservation mechanism reduced test waiting time by 30%. The designed high-precision test fixtures were successfully put into production and use, significantly improving the accuracy and stability of sensor testing. The newly built test bench for the actual measurement mechanism of simulators met the testing needs of new products, effectively supporting the R&D progress of BM4120 sensors. Aiming at potential hidden dangers found in the production line, the test process was optimized, reducing the test failure rate by 15% and ensuring the smooth progress of testing work.

Technical Skills: Systematically mastered hardware testing methods and reliability testing processes for MEMS sensors, improved Python data processing capabilities, and able to independently build automated data processing pipelines. Familiar with the design principles and processing technology of test fixtures, understood the debugging methods of precision instruments, and possessed the full-process capability from test scheme design to actual execution. At the same time, mastered the skills of test data visualization and dynamic dashboard construction, able to transform complex test data into intuitive analysis results.

Soft Skills: Cultivated the design and optimization thinking of precision test processes, learned how to improve work efficiency while ensuring test accuracy. Enhanced business communication and project coordination abilities through communication and collaboration with suppliers and cross-departmental teams. When solving various problems encountered in the testing process, exercised the ability of problem location and systematic solution design, and improved the ability of technical document and standardized guideline writing.

As a research assistant intern, mainly participated in the R&D project of wearable medical devices, focusing on the independent research and development of dual-mode blood pressure monitoring systems. Responsible for designing and implementing a synchronous acquisition system for ECG/PPG on upper computer and embedded terminals, developing core blood pressure monitoring algorithms, and improving monitoring accuracy combined with signal processing technology. Meanwhile, built the first version of test application program to realize real-time reception and processing of monitoring data and visual display of blood pressure fluctuations. In terms of data modeling, feature extraction was performed on patients' physiological data based on EM-UKF noise reduction method, and key mechanical index models were established to provide data support for algorithm optimization. In addition, responsible for the hardware circuit design and debugging of wearable devices, as well as the development of medical clinical experimental data analysis platform.

Multiple key achievements were made during this research internship. Successfully developed core algorithms for blood pressure monitoring and produced functional prototypes, verifying the feasibility and accuracy of the algorithms. The built ECG/PPG synchronous acquisition system operated stably, capable of real-time acquisition and preliminary analysis of physiological signals. The signal noise reduction processing based on EM-UKF method improved the accuracy of blood pressure monitoring by 10%, effectively reducing the impact of environmental interference on measurement results. The designed hardware circuit of wearable medical devices passed functional tests, meeting the basic clinical application requirements. The developed medical experimental data analysis platform has been put into internal use in the research institute, providing data processing support for relevant scientific research projects.

Technical Skills: Mastered the development methods of embedded AI algorithms, familiar with the acquisition and processing processes of ECG/PPG signals, and able to independently complete the design and debugging of hardware circuits. Deeply understood the noise reduction algorithms for biomedical signals, especially the application of EM-UKF method in physiological signal processing. At the same time, improved medical data analysis capabilities, able to combine engineering technology with biomedical knowledge to solve practical problems in medical device R&D.

Soft Skills: Cultivated the execution and management capabilities of scientific research projects, learned how to decompose a complex R&D project into executable specific tasks. Improved the ability of interdisciplinary knowledge integration and application, able to combine knowledge from different fields such as electronic engineering, computer science and biomedicine to solve practical problems. Accumulated valuable experience in experimental design and data interpretation, and improved the ability of technical document writing and scientific research achievement reporting.