Set/Reset Bilaterally Controllable Resistance Switching Ga-doped Ge₂Sb₂Te₅ Long-Term Electronic Synapses for Neuromorphic Computing

Long-term plasticity of bio-synapses modulates the stable synaptic transmission that is quite related to the encoding of information and its emulation using electronic hardware is one of important targets for neuromorphic computing. Ge2Sb2Te5 (GST) based phase change random access memory (PCRAM) has become a strong candidate for complementary-metal-oxide-semiconductor (CMOS) compatible integrated long-term electronic synapses to cope with the high-efficient and low power consumption data processing tasks for neuromorphic computing. However, the performance of PCRAM electronic synapses is still quite limited due to the challenges in linear and continuous conductance regulation, which originates from the fast and uncontrollable resistance switching characteristic of conventional PCRAM for the data storage application. Here an in-depth study is reported on the impact of gallium (Ga) doping on GST (GaGST) structural properties and on the corresponding 0.13 mu m CMOS technology fabricated PCRAM integrated devices with a mushroom structure. The Ga doping effectively retarded the crystallization process of GST by augmenting the local disorder of GeTe4-nGen tetrahedron, which subsequently leads to the Set/Reset bilaterally controllable resistance switching of corresponding PCRAM devices. The optimized 6.5%GaGST electronic synapses demonstrate gradual resistance switching characteristics and a good multilevel retention feature and eventually exhibit outstanding long-term synaptic plasticity like potentiation/depression and spiking time dependent plasticity in four forms. Such long-term electronic synapses are applied to handwritten digits recognition (96.22%) and CIFAR-10 image categorization (93.6%) and attain very high accuracy for both tasks. These results provide an effective method to achieve high performance PCRAM electronic synapses and highlight the great potential of GaGST PCRAM as a component for future high-performance neuromorphic computing.