Switched capacitor techniques

  • Pickard S
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Abstract

Capacitive transducer circuits which are built on conventional printed circuit boards can use standard analog design techniques, but designs for implementation on silicon are best done with switched capacitor circuits using analog discrete-time approaches, as accurate resistors are unavailable on silicon. MOS technology has become the most widely used silicon fabrication technique, suitable for large-scale digital circuits with 10 6 transistors as well as small application-specific ICs for mixed analog and digital functions. MOS tran-sistors also make excellent switches, and switched capacitor implementations of analog circuits for capacitive sensors offer an excellent combination of accuracy, low production cost, and integration with digital and computer logic circuits. This chapter is an introduc-tion to switched capacitor technology. For an in-depth treatment, read Switched Capacitor Circuits by Allen and Sanchez-Sinencio [1984]. 11.1 ALTERNATE DESIGN TECHNIQUES 11.1.1 Digital signal processing DSP (digital signal processing) techniques are becoming more popular as powerful DSP microcomputers are becoming available for low cost. With a DSP implementation, the low-level signal from the sensor electrodes is lowpass filtered, digitized to the required precision and at the required sampling rate, and input to a DSP computer for processing. The output digital stream is converted back to a continuous analog signal, if needed, with a DAC (digital-to-analog converter) and an output reconstruction filter. DSP techniques are accurate and capable of handling complex algorithms, but development time and power consumption are unfavorable, and more silicon area is usually needed. If a local computer is needed for other purposes and has some unused cycles, the DSP method may be best. A very rough estimate of instructions-per-second compute power for capacitive sensor processing can be made: 177

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APA

Pickard, S. (1996). Switched capacitor techniques. In Adaptive Analog VLSI Neural Systems (pp. 181–199). Springer Netherlands. https://doi.org/10.1007/978-94-011-0525-5_10

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