Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication, and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
Silicon (Si) based complementary metal-oxide semiconductor (CMOS) technology has been the driving force of the information-technology revolution. However, scaling of CMOS technology as per Moore’s law has reached a serious bottleneck. Among the emerging technologies memristive devices can be promising for both memory as well as computing applications. Hybrid CMOS/memristor circuits with CMOL (CMOS + “Molecular”) architecture have been proposed to combine the extremely high density of the memristive devices with the robustness of CMOS technology, leading to terabit-scale memory and extremely efficient computing paradigm. In this work, we demonstrate a hybrid 3D CMOL circuit with 2 layers of memristive crossbars monolithically integrated on a pre-fabricated CMOS substrate. The integrated crossbars can be fully operated through the underlying CMOS circuitry. The memristive devices in both layers exhibit analog switching behavior with controlled tunability and stable multi-level operation. We perform dot-product operations with the 2D and 3D memristive crossbars to demonstrate the applicability of such 3D CMOL hybrid circuits as a multiply-add engine. To the best of our knowledge this is the first demonstration of a functional 3D CMOL hybrid circuit.
A series of breakthroughs in memristive devices have demonstrated the potential of using crossbar-based memristor arrays as ultra-high-density and low-power memory. However, their unique device characteristics could cause data disturbance for both read and write operations resulting in serious data reliability problems.This paper discusses such reliability issues in detail and proposes a comprehensive yet low area-/performance-/energyoverhead solution addressing these problems. The proposed solution applies asymmetric voltages for disturbance confinement, inserts redundancy for disturbance detection, and employs a refreshing mechanism to restore weakened data. The results of a case study show that the average overheads of area, performance and energy consumption for achieving data reliability, over a baseline unreliable memory system, are 3%, 4%, and 19% respectively.
We report on a rapid, 32-channel reflectance-difference (RD) spectrometer with sub-second spectra acquisition times and ΔR/R sensitivity in the upper 10(-4) range. The spectrometer is based on a 50 kHz photo-elastic modulator for light polarization modulation and on a lock-in amplifier for signal harmonic analysis. Multichannel operation is allowed by multiplexing the 32 outputs of the spectrometer into the input of the lock-in amplifier. The spectrometer spans a wavelength range of 230 nm that can be tuned to cover E(1) and E(1) + Δ(1) transitions for a number of III-V semiconductors at epitaxial growth temperatures, including GaAs, InAs, AlAs, and their alloys. We present two examples of real-time measurements to demonstrate the performance of the RD spectrometer, namely, the evolution of the RD spectrum of GaAs (001) annealed at 500 °C and the time-dependent RD spectrum during the first stages of the epitaxial growth of In(0.3)Ga(0.7)As on GaAs (001) substrates.
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