Electrical Characteristic Fluctuations in Sub-45nm CMOS Devices. - Fu-Liang Yang; Jiunn-Ren Hwang; Yiming Li 005 (CICC, 2006)

A unipolar-CMOS with recessed source/drain load. - Jyi-Tsong Lin; Hsuan-Hsu Chen; Kuan-Yu Lu; Chih-Hung Sun; Tung-Yen Lai; Fu-Liang Yang (ASICON, 2011)

A fully integrated hepatitis B virus DNA detection SoC based on monolithic polysilicon nanowire CMOS process. - Che-Wei Huang; Yu-Jie Huang; Pei-Wen Yen; Hsiao-Ting Hsueh; Chia-Yi Lin; Min-Cheng Chen; Chia-Hua Ho; Fu-Liang Yang; Hann-Huei Tsai; Hsin-Hao Liao; Ying-Zong Juang; Chorng-Kuang Wang; et al. (VLSIC, 2012)

A CMOS-Compatible Poly-Si Nanowire Device with Hybrid Sensor/Memory Characteristics for System-on-Chip Applications. - Min-Cheng Chen; Hao-Yu Chen; Chia-Yi Lin; Chao-Hsin Chien; Tsung-Fan Hsieh; Jim-Tong Horng; Jian-Tai Qiu; Chien-Chao Huang; Chia-Hua Ho; Fu-Liang Yang (Sensors, 2012)

Study of sub-5 nm RRAM, tunneling selector and selector less device. - Kai-Shin Li; Ming-Taou Lee; Min-Cheng Chen; Cho-Lun Hsu; J. M. Lu; C. H. Lin; C. C. Chen; B. W. Wu; Y. F. Hou; C. Yi. Lin; Y. J. Chen; T. Y. Lai; et al. (ISCAS, 2015)

Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy. - Bitewulign Kassa Mekonnen; Webb Yang; Tung-Han Hsieh; Shien-Kuei Liaw; Fu-Liang Yang (Biomed. Signal Process. Control., 2020)

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c. - Justin Chu; Wen-Tse Yang; Wei-Ru Lu; Yao-Ting Chang; Tung-Han Hsieh; Fu-Liang Yang (Sensors, 2021)