全忆阻器超低能耗单层感知器研究

构建人工神经网络（ANN）的核心是硬件实现人脑突触和神经元功能的模拟。忆阻器能很好模拟突触和神经元的功能，并且易于高密度集成，因此是构建ANN的理想元器件。目前，高密度ANN的硬件实现依然是一个挑战，尤其是考虑到器件的能耗。人脑突触和神经元单次放电能耗低至fJ量级，但是绝大部分忆阻型类脑器件，单脉冲能耗远高于fJ量级，大量焦耳热将严重影响器件正常工作。在我们前期工作中发现，基于硫化物忆阻器的类脑器件，其单脉冲能耗最低可以达到fJ量级。本项目将进一步优化硫化物材料种类、改善材料微结构和忆阻器件结构，获得性能稳定的超低能耗（1-10 fJ）突触和神经元器件。在此基础上，制备无串扰交叉矩阵结构突触和神经元阵列，结合外围电路，构建全忆阻器超低能耗单层感知器，并且通过简单图像识别，测试单层感知器的有效性、稳定性和可靠性。

The core of constructing artificial neural networks (ANNs) is the hardware implementation of emulation of synaptic and neural functions. Memristor is ideal for the construction of ANNs since it is good synaptic and neural emulator and has simple structure. At present, it is still a big challenge to hardware implement ANNs with high device density, especially considering the energy dissipation of synaptic and neural devices. In the human brain, the energy dissipation of synapses and neurons is as low as several fJ per spike. However, the energy dissipation of most memristive neuromorphic devices is far higher than that of biological ones. Then, a large amount of Joule heat will severely deteriorate the device performance. Our previous study demonstrates that the lowest energy dissipation of neuromorphic devices based on sulfide memristors can reach several fJ. In this project, in order to obtain stable memristive synapses and neurons with ultra-low energy dissipation (1-10 fJ), the microstructure of sulfide materials and the structure of memristive devices will be improved. Next, crosstalk-free crossbar structured synaptic and neural arrays will be constructed. Finally, a simple all-memristive artificial neural network, i.e., single-layer perceptron, will be built. The performance of as-constructed perceptron including effectiveness, stability and reliability will be tested by simple pattern recognition.