Title :
Teaching Memory Circuit Elements via Experiment-Based Learning
Author :
Pershin, Y.V. ; Ventra, M.D.
Author_Institution :
Dept. of Phys. & Astron., Univ. of South Carolina, Columbia, SC, USA
Abstract :
The class of memory circuit elements which comprises memristive, memcapacitive, and meminductive systems, is gaining considerable attention in a broad range of disciplines. This is due to the enormous flexibility these elements provide in solving diverse problems in analog/neuromorphic and digital/ quantum computation; the possibility to use them in an integrated computing-memory paradigm, massively-parallel solution of different optimization problems, learning, neural networks, etc. The time is therefore ripe to introduce these elements to the next generation of physicists and engineers with appropriate teaching tools that can be easily implemented in undergraduate teaching laboratories. In this paper, we suggest the use of easy-to-build emulators to provide a hands-on experience for the students to learn the fundamental properties and realize several applications of these memelements. We provide explicit examples of problems that could be tackled with these emulators that range in difficulty from the demonstration of the basic properties of memristive, memcapacitive, and meminductive systems to logic/computation and crossbar memory. The emulators can be built from off-the-shelf components, with a total cost of a few tens of dollars, thus providing a relatively inexpensive platform for the implementation of these exercises in the classroom. We anticipate that this experiment-based learning can be easily adopted and expanded by the instructors with many more case studies.
Keywords :
electronic engineering education; memristors; student experiments; analog/neuromorphic computation; computing-memory paradigm; crossbar memory; digital/quantum computation; easy-to-build emulator; experiment-based learning; hands-on experience; memcapacitive system; memelements; meminductive system; memory circuit element; memristive system; teaching tool; undergraduate teaching laboratories; Memory management; Memristors; Neural networks; Neuromorphics; Next generation networking; Optimization; Quantum computing;
Journal_Title :
Circuits and Systems Magazine, IEEE
DOI :
10.1109/MCAS.2011.2181096