Symbolic analysis of tau cell log-domain filter using affine MOSFET models

This paper analyses a filter known as the Tau Cell using symbolic methods and shows that the operation of this filter is independent of the magnitude of the input DC offset. This means that the circuit places no restrictions on whether the input DC offset is a sub-threshold current or not. The circuit behaviour predicted from symbolic analysis was observed in similar circuits on a chip fabricated using MOSIS AMI 1.6μm technology. This paper highlights the utility of symbolic analysis and shows that it is a powerful tool for circuit analysis and design

A comparison of interval methods in symbolic circuit analysis applications

Symbolic circuit analysis involves deriving symbolic expressions for performance measures, such as voltage gain, input impedance, and evaluating them to obtain more insight into the behaviour of a circuit. In modern semiconductor technologies, it is more useful to evaluate the symbolic expressions using interval methods in order handle variations in parameter values. We compare the performance of different interval methods in evaluating symbolic expressions. Our experiments show that Generalised Interval Arithmetic is the most efficient method in affne form for our application. However, this method should be modified to suit long chains of computation. Our modification yields tighter interval bounds compared with other interval methods

A comparison of interval methods in symbolic circuit analysis applications

Symbolic circuit analysis involves deriving symbolic expressions for performance measures, such as voltage gain, input impedance, and evaluating them to obtain more insight into the behaviour of a circuit. In modern semiconductor technologies, it is more useful to evaluate the symbolic expressions using interval methods in order handle variations in parameter values. We compare the performance of different interval methods in evaluating symbolic expressions. Our experiments show that Generalised Interval Arithmetic is the most efficient method in affine form for our application. However, this method should be modified to suit long chains of computation. Our modification yields tighter interval bounds compared with other interval methods. References Francisco Fernandez et al., Symbolic Analysis Techniques Applications to Analog Design Automation IEEE Press, 1998. L. Kolev, Optimal Multiplication of G-intervals Reliable Computing, 13, pp.399--408, 2007. L. Kolev, New Formulae for Multiplication of Intervals, Reliable Computing, 12, pp.281--292, 2006. F. Messine and A. Touhami, A General Reliable Quadratic Form: An Extension of Affine Arithmetic, Reliable Computing, 12, pp.171--192, 2006. Xuan-Ha Vu, Rigourous solution techniques for numerical constraints satisfaction problems, PhD thesis no. 3155 (2005), Swiss Federal Institute of Technology, Lausanne, Switzerland 2005. G. Manson, Calculating frequency response functions for uncertain systems using complex affine analysis, J. Sound and Vibration, 288, pp.487--521, 2005. L. H. d. Figueiredo and J. Stolfi, Self-Validated Numerical Methods and Applications, IMPA, Rio de Janeiro, Brazil, July 1997. B. Thanigaivelan et al., A modified mosfet small-signal model based on Affine Arithmetic concepts, Proceedings of Asia Pacific Conference on Postgraduate Research In Microelectronics and Electronics, Shangai, China, 2009. Bozena Kaminska et al. Analog and Mixed-Signal Benchmark Circuits-First Release Proceedings of ITC, pp.183--190, 1997 S. M. Rump. (July 2010). Interval Laboratory. http://www.ti3.tu-harburg.de/rump/intlab

A low power neural recording amplifier with programmable gain and bandwidth

In this paper we present the design and implementation of a low power neural amplifer which has two programmable gains in two programmable bandwidths. The bandwidths are programmable between 0.7–300Hz, suitable for measuring local field potentials and 1.95–5.4kHz, suitable for measuring action potentials. The amplifier achieves a maximum gain of 79dB in the higher bandwidth. A chip has been designed and implemented using a 0.5_m technology with 8 neural amplifiers. On average the neural amplifier consumes less than 14_W at 3.3V

An 8-channel neural recording system with programmable gain and bandwidth

In this paper we present the design and implementation of an 8-channel programmable neural recording system that can be used to study and record electrophysiological data of freely behaving animals. Each neural amplifier has two programmable gains in two programmable bandwidths. The bandwidths are programmable between 0.1-300Hz, suitable to measure local field potentials and 0.3-4kHz, suitable for measuring action potentials. The programmable gains are 54dB, 66dB in the lower bandwidth region and 80dB, 86dB in the higher bandwidth region. A chip has been designed and implemented using a 0.5μm technology working at 3.3V

Symbolic analysis of the Tau cell log-domain filter using affine MOSFET models

This paper analyses a filter known as the Tau Cell using symbolic methods and shows that the operation of this filter is independent of the magnitude of the input DC offset. This means that the circuit places no restrictions on whether the input DC offset is a sub-threshold current or not. The circuit behaviour predicted from symbolic analysis was observed in similar circuits on a chip fabricated using MOSIS AMI 1.6μm technology. This paper highlights the utility of symbolic analysis and shows that it is a powerful tool for circuit analysis and design