Heba Abunahla earned her PhD from Khalifa University at UAE in 2017, her M.S. degree from University of Sharjah, UAE, and BS degree from United Arab Emirates University, UAE, all in ECE. Heba has several publications in the area of memristor devices and photovoltaic modules.

Dr. Baker Mohamamd earned his PhD from University of Texas at Austin, his M.S. from Arizona State University, Tempe, and BS from the University of New Mexico, Albuquerque, all in ECE. Dr. Mohammad is currently an associate Professor at the Department of Electrical and Computer Engineering at Khalifa University and a founding and active member of Khalifa University Semiconductor Research Center. He is a Senior Member of the IEEE and serves as an editor to the microelectronics journal, Elsevier. Baker Served in many organization and technical committee for IEEE conferences, In addition, he is a frequent reviewer for many journals including IEEE TVLSI, IEEE Circuits and Systems, and Springer. Baker has extensive experience for attracting and managing research grants including SRC, ADEC, UAE Space Agency and KU internal funding.

Prior to joining Khalifa University Baker has over 16-years industrial experience working for intel and Qualcomm in microprocessor design with emphasis on embedded system, and low power design. His research interest includes power efficient computing, high yield embedded memory, emerging technology such as memristor, STTRAM, computer architecture, and In-Memory-Computing. In addition, he is engaged in micro-watt range computing platform for WSN focusing on energy harvesting and power management including efficient dc/dc, ac/dc convertors. He authored/co-authored over 80 referred journals and conference proceedings, 2 books, 18 US patents, multiple invited seminars/panelist, and the presenter of 3 conference tutorials including one tutorial on Energy harvesting and Power management for WSN at the 2015 International Symposium on Circuits and Systems conference (ISCAS).

This book provides readers with a single-source guide to fabricate, characterize and model memristor devices for sensing applications. The authors describe a correlated, physics-based model to simulate and predict the behavior of devices fabricated with different oxide materials, active layer thickness, and operating temperature. They discuss memristors from various perspectives, including working mechanisms, different synthesis methods, characterization procedures, and device employment in radiation sensing and security applications.