A 16-b 10Msample/s Split-Interleaved Analog to Digital Converter

This work describes the integrated circuit design of a 16-bit, 10Msample/sec, combination ‘split’ interleaved analog to digital converter. Time interleaving of analog to digital converters has been used successfully for many years as a technique to achieve faster speeds using multiple identical converters. However, efforts to achieve higher resolutions with this technique have been difficult due to the precise matching required of the converter channels. The most troublesome errors in these types of converters are gain, offset and timing differences between channels. The ‘split ADC’ is a new concept that allows the use of a deterministic, digital, self calibrating algorithm. In this approach, an ADC is split into two paths, producing two output codes from the same input sample. The difference of these two codes is used as the calibration signal for an LMS error estimation algorithm that drives the difference error to zero. The ADC is calibrated when the codes are equal and the output is taken as the average of the two codes. The ‘split’ ADC concept and interleaved architecture are combined in this IC design to form the core of a high speed, high resolution, and self-calibrating ADC system. The dual outputs are used to drive a digital calibration engine to correct for the channel mismatch errors. This system has the speed benefits of interleaving while maintaining high resolution. The hardware for the algorithm as well as the ADC can be implemented in a standard 0.25um CMOS process, resulting in a relatively inexpensive solution. This work is supported by grants from Analog Devices Incorporated (ADI) and the National Science Foundation (NSF)

Accelerated neuromorphic cybernetics

Accelerated mixed-signal neuromorphic hardware refers to electronic systems that emulate electrophysiological aspects of biological nervous systems in analog voltages and currents in an accelerated manner. While the functional spectrum of these systems already includes many observed neuronal capabilities, such as learning or classification, some areas remain largely unexplored. In particular, this concerns cybernetic scenarios in which nervous systems engage in closed interaction with their bodies and environments. Since the control of behavior and movement in animals is both the purpose and the cause of the development of nervous systems, such processes are, however, of essential importance in nature. Besides the design of neuromorphic circuit- and system components, the main focus of this work is therefore the construction and analysis of accelerated neuromorphic agents that are integrated into cybernetic chains of action. These agents are, on the one hand, an accelerated mechanical robot, on the other hand, an accelerated virtual insect. In both cases, the sensory organs and actuators of their artificial bodies are derived from the neurophysiology of the biological prototypes and are reproduced as faithfully as possible. In addition, each of the two biomimetic organisms is subjected to evolutionary optimization, which illustrates the advantages of accelerated neuromorphic nervous systems through significant time savings