  Activation functions are widely used in neural networks to decide the
activation value of the neural unit based upon linear combinations of the
weighted inputs. The effective implementation of activation function is highly
important, as they help to represent non-linear complex functional mappings
between inputs and outputs of the neural network. One of the non-linear
approaches is to use a sigmoid function. Therefore, there is a growing need in
enhancing the performance of sigmoid circuits. In this paper, the main
objective is to modify existing current mirror based sigmoid model by replacing
CMOS transistors with memristor devices. This model was tested varying
different circuit parameters, transistor size and temperature. The the area,
power and noise in the modified CMOS-memristive sigmoid circuit are shown. The
application of memristors in the sigmoid circuit results in higher component
density in an on-chip area, allowing a reduction of power and area by 7$\%$.
The proposed sigmoid circuit was simulated in SPICE using 180nm TSMC CMOS
technology.
