This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, motion circuits) and an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions.
Understanding of the characteristics of neuromorphic circuit elements and their interaction in parallel networks.
Neuromorphic circuits are inspired by the structure, function and plasticity of biological neurons and neural networks. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. The high parallelism and connectivity of neuromorphic circuits permit structures with massive feedback without iterative methods and convergence problems and real-time processing networks for high-dimensional signals (e.g. vision). Application domains of neuromorphic circuits include silcon retinas and cochleas, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, multipliers, power-law circuits, resistive networks, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina, motion circuits) and an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems.
S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications.
Prerequisites / Notice
Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception and layout of such circuits with a set of inexpensive software tools, ending with an optional submission of a mini-project for CMOS fabrication.
Prerequisites: Background in basics of semiconductor physics helpful, but not required.