Unravelling new principles of site-selective doping contrast in the dual-beam focused ion beam/scanning electron microscope

Chee, Augustus K. W.

doi:10.1016/j.ultramic.2020.112947

Cited by 12 publications

(11 citation statements)

References 31 publications

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“…For the freshly cleaved specimen, p was brighter than n under standard imaging conditions (Fig. 1b), as expected (Oatley & Everhart, 1957; Perovic et al, 1995, 1998; Venables et al, 1998; Elliott et al, 2000; Sealy et al, 2000; Elliott, 2001; Elliott et al, 2002; Lin & Lee, 2003; El-Gomati et al, 2004; Buzzo et al, 2006, 2007; Chee et al, 2008 a , 2008 b ; Chee et al, 2010, 2011; Liu & Lee, 2011 a , 2011 b ; Chung et al, 2011; Farrell et al, 2013; Xu et al, 2013; Druckmüllerová et al, 2014; Chee, 2016 a ; Alugubelli et al, 2019; Chee, 2019, 2020). Moreover, surface treatment using fresh (degraded) NH 4 F (aq.)…”

Section: Resultsmentioning

confidence: 99%

“…The fraction of SEs generated within the crystalline region enclosed by the electron range will contribute to the site-specific doping contrast after diffusing through the surface amorphous material, surmounting the respective surface barriers, and percolating through the surface boundaries that will define the angles of emission when escaping the specimen surface; most SEs eventually escape from a depth 5 to 10 nm below the Si surface (Chee, 2019). Given that the surface amorphous layer thickness scales directly with the ion beam energy (Chee et al, 2010; Chee, 2020), contrast reduces (increases) with higher (lower) energy Ga + polishing (cf. Fig.…”

Section: Discussionmentioning

confidence: 99%

“…We further note that electron holography experiments incontrovertibly revealed an “electrically dead” surface damage layer at equipotential invoking patch field suppression (Twitchett et al, 2002), and so if doping contrast cannot occur without patch fields, no contrast should, in principle, be expected from Ga + ion-milled homo(hetero)junction specimens because of the equipotential surface. Instead, strong doping contrast can be demonstrated following low-kV final polishing to minimize the electrically inactive surface damage layer (Chee et al, 2010; Chee, 2020).…”

Section: Introductionmentioning

confidence: 99%

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The Mechanistic Determination of Doping Contrast from Fermi Level Pinned Surfaces in the Scanning Electron Microscope Using Energy-Filtered Imaging and Calculated Potential Distributions

Chee

2022

Microscopy and Microanalysis

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Secondary electron (SE) doping contrast in the scanning electron microscope is correlated with Fermi level pinned surfaces of Si samples prepared using HF-based wet-chemical treatment or focused ion beam (FIB) micromachining en route to quantitative dopant profiling. Using energy-resolved SE imaging techniques and finite-element analyses of surface states and surface junction potentials, we clarified the surface band-bending effects post-NH4F-treatment, consistent with brighter p-contrast from degenerately doped (>1019 cm−3) regions. In general, SE spectromicroscopy scan measurements unambiguously indicated heavy suppression of patch fields, while the empirical discovery of scan frequency-modulated contrast inversion due to Chee et al. [Springer Proceedings in Physics,120, pp. 407–410 (2008)] is ascribable to competing fixed oxide charge and dynamic charge injection phenomena (particularly at dwell times >29 μs). Leveraging numerical simulations of electric potentials and variable-voltage experimental data, the theoretical model based on amorphization damage-mediated Fermi level pinning is elucidated for Ga+ FIB-processed site-specific doping contrast on patch field-free surfaces. This work successfully argues against the notion that doping contrast ultimately or exclusively entails patch fields or adventitious metal–semiconductor contacts.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Mechanistic Determination of Doping Contrast from Fermi Level Pinned Surfaces in the Scanning Electron Microscope Using Energy-Filtered Imaging and Calculated Potential Distributions

Chee

2022

Microscopy and Microanalysis

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“…Passivation technology must be further investigated for formation of doped region with a high activation ratio and a low density of defect states. Moreover, microscopic investigation such as high-resolution secondary ion microscopy will be necessary to make the physics of defect-assisted activation of dopant atoms and criteria of reduction of recombination defect formation [21].…”

Section: Resultsmentioning

confidence: 99%

Argon Precursor Ion Implantation Used to Activate Boron Atoms in Silicon at Low Temperatures

et al. 2020

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The two-step implantation of argon precursor ion (Ar + ) followed by boron ion (B + ) in single crystalline silicon at room temperature is discussed to activate boron implanted region by post heating at 300 followed by 400 o C. The implantation of Ar + at a dose of 6.0 × 10 13 cm −2 at 70 keV with a projected range R p (Ar) of 80 nm followed by B + at 1.0 × 10 15 cm −2 and 15 keV with R p (B) of 62 nm caused crystalline disordered states with the effective disordered amorphous depth A eff of 32 nm, while the post heating of 300 o C for 90 min followed by 400 o C for 30 min markedly decreased A eff to 1.8 nm. The effective recrystallization by the post heating promoted activation of doped region associated with decrease in the sheet resistivity to 189 /sq by the post heating. The activation ratio was estimated as 0.33 under the assumption of a hole mobility of 50 cm 2 /Vs in the boron implanted region.INDEX TERMS Activation, carrier density, carrier lifetime, defect, low temperature.

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“…However, this is impractical because every measuring process added between each pair of contiguous processes can significantly increase the total time of production [4]. Combined focused ion beam and SEM could be a possibility for on-line metrology [5].…”

Section: Introductionmentioning

confidence: 99%

Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed- Forward Neural Network Model for Semiconductor Manufacturing

Kim

2020

IEEE Access

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In semiconductor manufacturing, maintaining a high yield and ensuring accurate yield prediction are considerably important for improving productivity, customer satisfaction, and enhancing profitability. Despite its importance and merits, achieving wafer yield prediction with high quality and accuracy is challenging. In this paper, we propose a method for wafer edge yield prediction using a combined long short-term memory (LSTM) and feed-forward neural network (FFNN) model. Unlike previous research, we focus on the edge yield because of the higher yield loss at the wafer edge. The combined LSTM-FFNN model uses a dataset divided into two types according to data characteristics. Timeseries data are used in the case of LSTM, and non-time-series data are fed into the FFNN. When preparing the time-series data, comprising data related to the equipment and chambers, data of different chambers do not overlap, thereby rendering them as independent entities. The proposed model outperforms other models in terms of all evaluation metrics. The coefficient of determination of the proposed combined LSTM-FFNN model is 34.14%, which is almost 13% higher than that of the other compared models on average.

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Unravelling new principles of site-selective doping contrast in the dual-beam focused ion beam/scanning electron microscope

Cited by 12 publications

References 31 publications

The Mechanistic Determination of Doping Contrast from Fermi Level Pinned Surfaces in the Scanning Electron Microscope Using Energy-Filtered Imaging and Calculated Potential Distributions

The Mechanistic Determination of Doping Contrast from Fermi Level Pinned Surfaces in the Scanning Electron Microscope Using Energy-Filtered Imaging and Calculated Potential Distributions

Argon Precursor Ion Implantation Used to Activate Boron Atoms in Silicon at Low Temperatures

Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed- Forward Neural Network Model for Semiconductor Manufacturing

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