Experienced staff firmware engineer with over 10 years working in the semiconductor industry. Skilled in embedded firmware by C language, algorithm design and development in MCUs. Currently responsible for the touch firmware, algorithm design and feature development of smartphones based on ARM M3/M4.Successful function implementation and application as>> Machine learning applied to touch MCUsReal-time prediction of lattice data noise coming from the display panel or external sources (ex: AMOLED of Y-OCTA and F-OCTA, plug-in charger, wireless charger, function generator, …, etc.) and automatically selects a good filter model to cancel noisy data through automatic correction. Applied to digital signal data can provide as well as good noise immunity and reduce power consumption in MCU.>> Adaptive power saving applied to touch MCUs. Real-time dynamic processing of user operations and prediction of lattice data noise from the display panel or external sources to provide lower power consumption during MCU idle and active modes. The power saving can be improved through machine learning.>> Digital signal processing applied to touch MCUsApplication to general or foldable AMOLED panels can provide as well as good signal lattice data of noise immunity and maintain as well as good signal-to-noise ratio. Application to coordinate reporting can provide smooth response coordinate data and low drag latency characteristics, as well as good noise immunity.>> Linear regression models applied to touch MCUs. Application to digital signal lattice data can provide response data of different order filter models to cancel noise data. Application to 2D data can provide tracking prediction data and smooth coordinate reporting.