Hardware-based Neural Networks using a Gated Schottky Diode as a Synapse Device

A gated Schottky diode is proposed for high-performance synapse devices and a means of designing a neural network using this device is described. The proposed gated Schottky diode operates in the saturation region with respect to the input voltage and is therefore immune to input noise and enables accurate vector-by-matrix multiplication. Moreover, by applying identical pulses to the bottom gate to store charges in a storage layer, the reverse saturation current increases almost linearly. Considering these special characteristics, we propose an architecture that uses a time-modulated input pulse and a learning rule based on a single conductance step. A three-layer perceptron network is trained using the conductance response of the synapse device and unidirectional weight-updating methods. In simulations using this network, the classification accuracy rate of MNIST training sets was found to be 94.50%. Compared to memristive devices, the improved linearity of the conductance response in our device is evidence of its higher accuracy.

[1]  Chi-Sang Poon,et al.  Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities , 2011, Front. Neurosci..

[2]  Jong-Ho Lee,et al.  Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices , 2018, Neural Computing and Applications.

[3]  G. W. Burr,et al.  Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element , 2015, 2014 IEEE International Electron Devices Meeting.

[4]  Damien Querlioz,et al.  Learning with memristive devices: How should we model their behavior? , 2011, 2011 IEEE/ACM International Symposium on Nanoscale Architectures.

[5]  Yoon-Ha Jeong,et al.  Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems , 2015, IEEE Electron Device Letters.

[6]  H. Hwang,et al.  Optimized Programming Scheme Enabling Linear Potentiation in Filamentary HfO2 RRAM Synapse for Neuromorphic Systems , 2016, IEEE Transactions on Electron Devices.

[7]  Tarek M. Taha,et al.  Neuromemristive Systems: Boosting Efficiency through Brain-Inspired Computing , 2016, Computer.

[8]  Shimeng Yu,et al.  Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.

[9]  Byung-Gook Park,et al.  High-Density and Near-Linear Synaptic Device Based on a Reconfigurable Gated Schottky Diode , 2017, IEEE Electron Device Letters.

[10]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[11]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[12]  Sapan Agarwal,et al.  Li‐Ion Synaptic Transistor for Low Power Analog Computing , 2017, Advanced materials.