DEPARTMENT.FACULTY

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Dr. Ahmed Shaban
  • DEPARTMENT_STAFF.QUALIFICATION

    PhD

  • DEPARTMENT_STAFF.DESIGNATION

    Guest Faculty

  • DEPARTMENT_STAFF.THRUST_AREA

    Neuromorphic computing, In-memory computing, eNVM based device circuit co-design, RRAM, STT-MRAM, SOT-MRAM

  • DEPARTMENT_STAFF.ADDRESS

  • DEPARTMENT_STAFF.MOBILE

    8791027146

  • DEPARTMENT_STAFF.EMAIL

    ahmedshaban@zhcet.ac.in

DEPARTMENT_STAFF.PROFILE

Ahmed Shaban pursued his Ph.D. degree as part of the joint doctoral program between the Indian Institute of Technology Delhi (IITD) and National Yang-Ming Chiao Tong University (NYCU), Taiwan. He was awarded the Junior Research Fellowship from the University Grants Commission (UGC), India, in 2018, for pursuing a PhD. He also received the MediaTek industry fellowship to support his PhD research in Taiwan. He received his B.Tech degree in Electronics Engineering and M.Tech degree in Electronic circuits and system design from the Department of Electronics Engineering, Zakir Husain College of Engineering and Technology (ZHCET), Aligarh Muslim University (AMU), Aligarh, India. His research focuses on leveraging emerging non-volatile memory technologies, such as resistive RAM (RRAM) and magnetic RAM (MRAM), for the development of energy-efficient circuits and hardware for neuromorphic computing, in-memory acceleration of neural networks and highly parallel in-memory search operations using non-volatile content-addressable memories.


  1. Ahmed Shaban, Sai Sukruth Bezugam, and Manan Suri. An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation. Nature Communications, 12(1):4234, 2021.
  2. Ahmed Shaban, Tuo-Hung Hou, and Manan Suri. SOT-MRAM-based approximate content addressable memory for DNA classification. IEEE Transactions on Electron Devices, 71:5732–5738, 2024. doi: 10.1109/TED.2024.3423812.
  3. Ahmed Shaban, Shreshtha Gothalyan, Tuo-Hung Hou, and Manan Suri. SOT-MRAM based design for energy-efficient and reliable binary neural network acceleration. IEEE Transactions on Electron Devices, 71:5367–5374, 2024. doi: 10.1109/TED.2024.3435810.