activation functions in multiple layers in CNNs
Not only for Convolutional Neural Networks (CNNs), also for DNNs (Deep Neural Networks) and RNNs (Recurrent Neural Networks), we use activation functions at every layer. Sigmoid (for binary classification), softmax (for multiclass classification) or some other types are usually used at the final output layer, each specific for the kind of labels that we have to compare with the predictions. 

However, other neurons require activation functions as well, especially for nonlinearity purposes; most popular ones are ReLUs(Rectified Linear Units), Leaky ReLU, tanh,.. and so on. We nearly always use an activation function for every neuron in Deep Learning. For a detailed insight, have a look at:

https://www.youtube.com/watch?v=Xvg00QnyaIY

Also specificly for Convolutional Nets:

https://www.youtube.com/watch?v=jPOAS7uCODQ&amp;list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF&amp;index=7

Hope I could help, please do not hesitate to ask more. Good Luck!