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Experimental studies of long-range atomic H motion and desorption in hydrogenated amorphous silicon and germanium
Long-range H motion and desorption in low hydrogen concentration undoped hydrogenated amorphous silicon (a-Si:H) and germanium (a-Ge:H) was studied by deuterium secondary ion mass spectrometry (SIMS) depth profiles and IR absorption of a-Si:H/a-Si:(H,D)/a-Si:H and a-Ge:H/a-Ge:(H,D)/a-Ge:H. SIMS monitors deuterium motion (assumed similar to that of H), while IR yields information on hydrogen content and bonding. The diffusion constant was found to be dispersive with time, and depended on H content C[subscript] H, diffusion length L, and microvoid content, at temperatures T ≤ 400°C for a-Si:H and T ≤ 310°C for a-Ge:H. It exhibited a power-law D(t) = D[subscript]oo([omega] t)[superscript]-[alpha] relation in both systems. In a-Si:H, [alpha] generally deviates from the 1 - T/T[subscript] o dependence on the temperature T expected from a multiple trapping mechanism. The diffusion constant at constant diffusion length D(t[subscript] L) then deviates from an Arrhenius dependence on the temperature. The apparent activation energy E[subscript] a and prefactor D[subscript] o, defined by the linear best-fit of lnD(t[subscript] L) vs 1/T, strongly increase with L at low C[subscript] H. The Meyer-Neldel relation (MNR) D[subscript] o = [macron] A[subscript]ooexp(E[subscript] a/T[subscript] o[superscript]\u27), where [macron] A[subscript]oo ~eq 3.1 x 10[superscript]14 cm[superscript]2/s and T[subscript] o[superscript]\u27 ~eq 730 K, holds for all 1.3≤ E[subscript]a≤ 2.4 eV and 2.5x 10[superscript]-5≤ D[subscript] o≤ 3100 cm[superscript]2/s;The a-Ge:H, [alpha] is essentially temperature and composition independent, but increases with microvoid content. The activation energy E[subscript] a ranges from 0.7 to 1.2 eV among the various films. The Meyer-Neldel relation is observed, with [macron] A[subscript]oo~eq 5.5x 10[superscript]-16 cm[superscript]2/s and T[subscript] o, ~eq 530 K. These values are lower than the corresponding values in a-Si:H. Hydrogen desorption temperature is as low as 180°C. Yet the significance of the MNR is questionable in both a-Si:H and a-Ge:H;The diffusion results for both a-Si:H and a-Ge:H are discussed in relation to the microstructure of the films. The nature of the microvoid-induced deep H-trapping sites is also discussed. Finally, a possible relation between the dispersive diffusion and a percolation model is presented. ftn*DOE Report IS-T-1559. This work was performed under contract No. W-7405-Eng-82 with the U.S. Department of Energy
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
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