Analog CMOS implementation of a CNN-based locomotion controller with floating-gate devices

This paper proposes an analog CMOS circuit that implements a central pattern generator (CPG) for locomotion control in a quadruped walking robot. Our circuit is based on an affine transformation of a reaction-diffusion cellular neural network (CNN), and uses differential pairs with multiple-input floating-gate (MIFG) MOS transistors to implement both the nonlinearity and summation of CNN cells. As a result, the circuit operates in voltage mode, and thus it is expected to reduce power consumption. Due to good matching accuracy of devices, the circuit generates stable rhythmic patterns for robot locomotion control. From experimental results on fabricated chip using a standard CMOS 1.5-(mu)m process, we show that the chip yields the desired results; i.e., stable rhythmic pattern generation and low power consumption.