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Compact low-power calibration mini-DACs for neural arrays with programmable weights.

B Linares-Barranco1, T Serrano-Gotarredona, R Serrano-Gotarredona

  • 1IMSE-CNM-CSIC, Inst. de Microelectron. de Sevilla, Spain.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary

Calibrating compact mini digital-to-analog converters (mini-DACs) significantly improves precision for large neural arrays. This technique enhances performance for low-resolution applications like neural processing circuits.

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Area of Science:

  • Electrical Engineering
  • Neuroscience Engineering

Background:

  • Compact, low-power mini digital-to-analog converters (mini-DACs) are crucial for large neural arrays requiring programmable weights.
  • Weak inversion transistor operation is necessary for low power consumption in large-scale integrated circuits but suffers from poor device matching.

Purpose of the Study:

  • To assess the viability of mini-DACs in large neural arrays.
  • To investigate methods for overcoming transistor mismatch limitations in weak inversion operation.
  • To enhance the precision of mini-DACs for applications in neural processing circuits.

Main Methods:

  • Utilizing transistors biased in weak inversion to achieve low currents and power consumption.
  • Implementing a calibration technique by pairing mini-DACs to compensate for inter-device mismatch.

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  • Developing a design methodology for achieving target precision in calibrated mini-DACs.

    • Main Results:

    • Initial fabrication of 50 mini-DACs showed an effective precision of 47% (1.1 bits) due to transistor mismatch.
    • The proposed calibration method improved precision by a factor of 10, reaching 4.8% (4.4 bits).
    • Simulation results demonstrated the successful calibration of an array of digitally controlled integrate-and-fire neurons.

    Conclusions:

    • Calibrated mini-DACs are viable for low-resolution applications, particularly in massive neural processing circuits.
    • The demonstrated calibration technique effectively compensates for transistor mismatch, significantly enhancing DAC precision.
    • A practical design methodology is provided for creating precise, calibrated mini-DACs for neural engineering applications.