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n this presentation we will discuss recent results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia Labs developed Whet stone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.

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TYPE Other Report YEAR 2021

DOI OSTI

A Model for Atomic Precision p-Type Doping with Diborane on Si(100)-2×1

Journal of Physical Chemistry C

Campbell, Quinn; Ivie, Jeffrey A.; Bussmann, Ezra; Schmucker, Scott W.; Baczewski, Andrew D.; Misra, Shashank

Diborane (B2H6) is a promising molecular precursor for atomic precision p-type doping of silicon that has recently been experimentally demonstrated [ Škereň et al. Nat. Electron. 2020 ]. We use density functional theory (DFT) calculations to determine the reaction pathway for diborane dissociating into a species that will incorporate as electrically active substitutional boron after adsorbing onto the Si(100)-2×1 surface. Our calculations indicate that diborane must overcome an energy barrier to adsorb, explaining the experimentally observed low sticking coefficient (<1 × 10-4 at room temperature) and suggesting that heating can be used to increase the adsorption rate. Upon sticking, diborane has an ≈50% chance of splitting into two BH3 fragments versus merely losing hydrogen to form a dimer such as B2H4. As boron dimers are likely electrically inactive, whether this latter reaction occurs is shown to be predictive of the incorporation rate. The dissociation process proceeds with significant energy barriers, necessitating the use of high temperatures for incorporation. Using the barriers calculated from DFT, we parameterize a Kinetic Monte Carlo model that predicts the incorporation statistics of boron as a function of the initial depassivation geometry, dose, and anneal temperature. Our results suggest that the dimer nature of diborane inherently limits its doping density as an acceptor precursor and furthermore that heating the boron dimers to split before exposure to silicon can lead to poor selectivity on hydrogen and halogen resists. This suggests that, while diborane works as an atomic precision acceptor precursor, other non-dimerized acceptor precursors may lead to higher incorporation rates at lower temperatures.

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TYPE Journal Article YEAR 2021

DOI OSTI Scopus

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