UC Santa Cruz

Artificial intelligence (AI) technology like deep learning is powering our daily life in many areas such as pattern recognition. The artificial neural network (ANN) is one of the deep learning models to achieve pattern recognition. A well-trained ANN can recognize images with precision over 98%. Although traditional two terminals memristors like phase changing memory (PCM) are already used to build ANNs. But those devices typically suffer from nonlinear, asymmetric conductance tuning problems. The novel memristive device Ionic Floating Gate memory (IFG) could potentially solve those problems. In this paper, a compact Cadence model of fully connected ANN using IFG is presented. The devices are tuned to an optimized state and formed a well-trained network. The recognition accuracy reaches 93.8%. This work demonstrates the IFG device also has the potential to be further utilized into other deep neural networks as synaptic memory.