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

A Two-Phase Maximum-Likelihood Sequence Estimation for Receivers with Partial CSI

The optimality of the conventional maximum likelihood sequence estimation (MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption that the receiver has perfect knowledge of the channel coefficients or channel state information (CSI). However, in practical situations that fail the assumption, the MLSE method becomes suboptimal and then exhaustive checking is the only way to obtain the ML sequence. At this background, considering directly the ML criterion for partial CSI, we propose a two-phase low-complexity MLSE algorithm, in which the first phase performs the conventional MLSE algorithm in order to retain necessary information for the backward VA performed in the second phase. Simulations show that when the training sequence is moderately long in comparison with the entire data block such as 1/3 of the block, the proposed two-phase MLSE can approach the performance of the optimal exhaustive checking. In a normal case, where the training sequence consumes only 0.14 of the bandwidth, our proposed method still outperforms evidently the conventional MLSE.Comment: 5 pages and 4 figure