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In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.

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This paper presents the current state of understanding of the factors that limit the continued scaling of Si complementary metaloxide-semiconductor (CMOS) technology and provides an analysis of the ways in which application-related considerations enter into the determination of these limits. The physical origins of these limits are primarily in the tunneling currents, which leak through the various barriers in a MOS field-effect transistor (MOSFET) when it becomes very small, and in the thermally generated subthreshold currents. The dependence of these leakages on MOSFET geometry and structure is discussed along with design criteria for minimizing short-channel effects and other issues related to scaling. Scaling limits due to these leakage currents arise from application constraints related to power consumption and circuit functionality. We describe how these constraints work out for some of the most important application classes: dynamic random access memory (DRAM), static random access memory (SRAM), low-power portable devices, and moderate and high-performance CMOS logic. As a summary, we provide a table of our estimates of the scaling limits for various applications and device types. The end result is that there is no single end point for scaling, but that instead there are many end points, each optimally adapted to its particular applications.

Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.

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