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Assistant Professor
Assistant Professor
Department of Smart Computing
Kyungdong University (Global Campus).
Address: 46, Bongpo4-gil, Toseong-myeon, Goseong-gun, Gangwon-do, Republic of Korea 24764.
Research Interests:
Memristor
Memristive Systems
Neuromorphic
Bioelectronics
Machine Learning
Deep Neural Networks
Educations:
Ph. D. – 2019-08 – Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Republic of Korea.
Topic: A Bioelectronics circuit design for the implementation of bio-synaptic transmission and plasticity.
M. Sc. – 2016-02 – Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Republic of Korea.
Topic: Design of an Oscillator with a First Order Generic Model of Memristor.
B. Sc. – 2010-04 – Department of Electrical and Electronics Engineering, East West University, Dhaka, Bangladesh.
Topic: Degradation of Effective Decoupling Capacitance in High Speed CMOS Circuits.
Achievements:
2019 – Certificate of Excellence (Jeonbuk National University)
In recognition of outstanding performance in scientific research during the Doctoral program.
2019: Outstanding Graduate Student Award (BK21 Plus Hope-IT Human Resource Development Center)
In recognition for contributing to the principles of graduate research while in pursuit of the Doctoral degree during Mar. 2018 – Feb. 2019.
2018: Outstanding Graduate Student Award (BK21 Plus Hope-IT Human Resource Development Center)
In recognition for contributing to the principles of graduate research while in pursuit of the Doctoral degree during Mar. 2017 – Feb. 2018.
2014-2016: Brain Korea 21 scholarship awarded by Jeonbuk National University, Republic of Korea.
2007-2008: Dean Scholarship awarded by East West University, Dhaka, Bangladesh.
Notable Publications:
Z. I. Mannan, S. P. Adhikari, C. Yang, R. K. Budhathoki, H. Kim, and L. Chua, “Memristive Imitation of Synaptic Transmission and Plasticity,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3458-3470, 2019.
This paper presents a memristive artificial neural circuit that imitates the excitatory chemical synaptic transmission of a biological synapse. The proposed memristor-based neural circuit exhibits synaptic plasticity, one of the most important neurochemical foundations for learning and memory, which is demonstrated via the efficient imitation of short-term facilitation (STF) and long-term potentiation (LTP). Moreover, the memristive artificial circuit also mimics the distinct biological attributes of strong stimulation and deficient synthesis of neurotransmitters. The proposed artificial neural model is designed in SPICE, and the biological functionalities are demonstrated via various simulations. The simulation results obtained with the proposed artificial synapse are similar to the biological features of chemical synaptic transmission and synaptic plasticity.
Biological Synaptic Transmission
Artificial Neuronal Network
Artificial circuit for bio-synaptic Transmission and plasticity
Z. I. Mannan, S. P. Adhikari, H. Kim, L. Chua, “Global dynamics of Chua Corsage memristor circuit family: fixed-point loci, Hopf bifurcation, and coexisting dynamic attractors,” Nonlinear Dynamics, vol. 99, pp. 3169-3196, 2020.
This paper presents an in-depth and rigorous mathematical analysis of a family of nonlinear dynamical circuits whose only nonlinear component is a Chua Corsage Memristor (CCM) characterized by an explicit seven-segment piecewise-linear equation. When connected across an external circuit powered by a DC battery, or a sinusoidal voltage source, the resulting circuits are shown to exhibit four asymptotically stable equilibrium points, a unique stable limit cycle spawn from a supercritical Hopf bifurcation along with three static attractors, four coexisting dynamic attractors of an associated non-autonomous nonlinear differential equation, and four corresponding coexisting pinched hysteresis loops. The basin of attractions of the above static and dynamic attractors is derived numerically via global nonlinear analysis. When driven by a battery, the resulting CCM circuit exhibits a contiguous fixed-point loci, along with its DC V–I curve described analytically by two explicit parametric equations. We also proved the fundamental feature of the edge of chaos property; namely, it is possible to destabilize a stable circuit (i.e., without oscillation) and make it oscillate, by merely adding a passive circuit element, namely L > 0. The CCM circuit family is one of the few known examples of a strongly nonlinear dynamical system that is endowed with numerous coexisting static and dynamic attractors that can be studied both experimentally, and mathematically, via exact formulas.
Family of 6-lobe CC memristor circuits
Trajectories of 6-lobe CC memristor circuits
Z. I. Mannan, C. Yang, S. P. Adhikari, H. Kim, “Exact analysis and physical realization of the 6-lobe Chua Corsage Memristor,” Complexity, vol. 2018 (Article ID-8405978), 2018.
A novel generic memristor, dubbed the 6-lobe Chua corsage memristor, is proposed with its nonlinear dynamical analysis and physical realization. The proposed corsage memristor contains four asymptotically stable equilibrium points on its complex and diversified dynamic routes which reveals a 4-state nonlinear memory device. The higher degree of versatility of its dynamic routes reveals that the proposed memristor has a variety of dynamic paths in response to different initial conditions and exhibits a highly nonlinear contiguous DC V-I curve. The DC V-I curve of the proposed memristor is endowed with an explicit analytical parametric representation. Moreover, the derived three formulas, exponential trajectories of state xn(t), time period tfn, and minimum pulse amplitude VA, are required to analyze the movement of the state trajectories on the piecewise linear (PWL) dynamic route map (DRM) of the corsage memristor. These formulas are universal, that is, applicable to any PWL DRM curves for any DC or pulse input and with any number of segments. Nonlinear dynamics and circuit and system theoretic approaches are employed to explain the asymptotic quad-stable behavior of the proposed corsage memristor and to design a novel real memristor emulator using off-the-shelf circuit components.
Power-off-plot (POP)
Power-off-plot (POP)
Parametric representation I = i(x)
Parametric representation I = i(x)
Parametric representation V = v(x)
Parametric representation V = v(x)
Circuit diagram of 6-lobe CC memristor emulator
Circuit diagram of 6-lobe CC memristor emulator
DC V-I plot of the 6-lobe CC memristor over input voltage −10V ≤ V ≤ 10 V. The left inset shows the conductance values at V= 0 V. The right inset shows the zoomed portion of the red DC V-I curve over the voltage range −5V ≤ V ≤ 2V.
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