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Improving Transmon Qubit Performance with Fluorine-based Surface Treatments

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Fluorine-based surface treatments that remove germanium residue from Josephson-junction fabrication double the median energy relaxation time of aluminum-on-silicon transmon qubits, to 334 microseconds.

desk verdict Genuinely useful process result with a real statistical caveat: wafer-level replication is not reported. read the letter →

arxiv 2507.08089 v1 pith:TJ3KXACV submitted 2025-07-10 quant-ph cond-mat.mes-hallcond-mat.mtrl-sci

Michael A. Gingras , Bethany M. Niedzielski , Kevin A. Grossklaus , Duncan Miller , Felipe Contipelli , Kate Azar , Luke D. Burkhart , Gregory Calusine
show 16 more authors
Daniel Davis Renée DePencier Piñero Jeffrey M. Gertler Thomas M. Hazard Cyrus F. Hirjibehedin David K. Kim Jeffrey M. Knecht Alexander J. Melville Christopher O'Connell Robert A. Rood Ali Sabbah Hannah Stickler Jonilyn L. Yoder William D. Oliver Mollie E. Schwartz Kyle Serniak
This is my paper · ORCID
classification quant-phcond-mat.mes-hallcond-mat.mtrl-sci PACS 85.25.Cp81.65.Cf
keywords transmonqubitsJosephsonjunctionfabricationfluorinesurfacetreatmentgermaniumcontaminationenergyrelaxationtimequalityfactorSTEM/EELSmaterialsanalysisshadowevaporationmask
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a major source of decoherence in aluminum-on-silicon transmon qubits sits at the metal-substrate interface directly under the Josephson junction, in the form of germanium-containing residue left behind by the shadow-mask fabrication process. The authors show that a fluorine-based wet etch (a commercial pad etch) applied to the silicon before junction deposition removes this residue, and that combining it with an in-vacuo argon ion mill produces a statistically significant twofold improvement in qubit quality. Their best process yields a median energy relaxation time $T_1 = 334\,\mu\mathrm{s}$ and quality factor $Q = 6.6 \times 10^6$, up from $T_1 = 157\,\mu\mathrm{s}$ and $Q = 3.3 \times 10^6$ for the process of record. If correct, this gives other qubit foundries a simple, targeted way to lift coherence by cleaning one specific interface rather than changing the base material or junction design.

What carries the argument

The mechanism that carries the argument is a pre-deposition surface-treatment sequence: a buffered fluorine-based pad etch (a commercial etchant containing HF, ammonium fluoride, and acetic acid with aluminum corrosion inhibitors) that strips oxide and residue from the exposed silicon at the junction footprint, followed by in-vacuo argon ion milling that cleans the surrounding silicon surface. The contamination it removes is produced by the CF$_4$ dry etch of a germanium hard mask in the suspended-bridge (Dolan-style) resist stack, whose byproducts are redeposited onto the silicon and remain trapped beneath the bridge where no line-of-sight ion mill can reach them. Materials analysis using cross-sectional scanning transmission electron microscopy with energy-dispersive X-ray and electron-energy-loss spectroscopy is the tool that identifies the germanium layer and verifies its removal.

What would settle it

Re-measure Process E on wafers after correcting the junction oxidation dose so that all 48 qubits meet frequency targets, and include every qubit in the median; if the all-inclusive median $Q$ drops to the process-of-record level near $3.3 \times 10^6$, the reported $2\times$ improvement is an artifact of dataset exclusion.

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Extended reading notes

Core claim

The central claim is that the loss limiting these fixed-frequency transmons is dominated by a contamination layer at the silicon-aluminum interface under the Josephson junction, and that this layer originates as germanium dry-etch byproducts from the Ge/MMA resist stack used to form the suspended shadow-evaporation mask. Cross-sectional STEM with EDS and EELS shows a germanium-bearing layer at that interface in untreated devices, absent after pad etch plus argon ion mill; top-down SEM of removed masks shows residue under the bridge that only the wet etch can reach, and residue on the open silicon that the ion mill removes. The authors attribute the measured $2\times$ improvement in median $Q$ ($6.6 \times 10^6$ versus $3.3 \times 10^6$) to removal of this residue, and note that removing the strap connections between junction leads and base metallization gives an additional significant improvement once the interface is clean.

Load-bearing premise

The reported $2\times$ improvement assumes that qubits excluded from the $T_1$ dataset, including those with low-$J_c$ frequency targeting or calibration failures that were more common on treated wafers, are not systematically worse than the qubits that were included.

Editorial extensions

If this is right

  • Other groups fabricating Al/AlOx/Al transmons with germanium-based shadow masks can apply the same pad-etch step without changing junction geometry, base metal, or oxidation parameters, and should expect a similar reduction in sub-junction interface loss.
  • Because the ion mill regrows a sub-junction oxide yet the pad-etch-plus-mill process still wins, the dominant loss before treatment was the contaminant layer rather than the oxide itself; future work can target the residue directly.
  • Eliminating strap connections adds a further significant quality-factor improvement once the interface is cleaned, pointing to strap processing as the next interface-loss bottleneck.
  • Combining device-level $T_1$ statistics with STEM, EDS, and EELS metrology provides a template for attributing qubit decoherence to specific fabrication residues.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If interface cleaning is the true cause, then a direct test would compare quality factors on wafers whose mask stack contains no germanium at all: the pad-etch step should then produce a much smaller improvement, since the presumed contaminant would never be introduced.
  • The authors report that low-$J_c$ frequency targeting and calibration failures were more common on treated wafers; a testable prediction is that correcting the junction oxidation parameters restores target frequencies without erasing the $Q$ gain, removing the current yield penalty.
  • Because pad etch attacks exposed aluminum only mildly, similar buffered-fluoride chemistries may be adaptable to niobium- or tantalum-base qubit processes, where sub-junction interfaces are also suspected loss sources.
  • The observation that $T_2^E$ stays well below the $2T_1$ limit suggests that once relaxation improves to $334\,\mu\mathrm{s}$, dephasing from residual resonator photons or two-level-system dynamics will become the next bottleneck; the paper does not resolve this channel.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This manuscript reports an empirical study of fluorine-based wet surface treatments (dilute HF and a commercial aluminum pad etch) applied to the silicon substrate surface immediately before Josephson-junction deposition in Al-based transmon qubits. Six process splits are compared across a dataset of 182 measured qubits. The central claim is that the combination of pad-etch surface treatment, pre-Junction Ar ion milling, and no strap connections (Process E) improves the median qubit quality factor by roughly 2x over the process of record (median Q = 6.6e6 and T1 = 334 us for E versus Q = 3.3e6 and T1 = 157 us for POR). Supporting materials characterization including STEM, EDS, EELS, and SEM is used to identify germanium-containing residues under the Dolan bridge that arise from the Ge-based shadow-mask dry etch, and to argue that the pad etch removes these residues, thereby reducing a dominant loss channel at the metal-substrate interface underneath the Josephson junction.

Significance. If the central claim holds, the paper identifies a specific, previously underappreciated fabrication-induced loss source (germanium dry-etch byproducts under the Dolan bridge) and demonstrates a simple, manufacturable process modification that more than doubles median transmon energy relaxation time. The study is notable for its scale (182 qubits), for combining device-level coherence statistics with independent materials analysis (STEM/EDS/EELS), and for transparently reporting which qubits were excluded from the analysis. However, the statistical case for the 2x improvement rests on qubit-level p-values from a Tukey-Kramer test applied to wafer-level process splits, and the paper does not report the number of wafers underlying each split. This missing replication information is the main load-bearing weakness; the exclusion pattern of qubits is a second, smaller concern. The materials-science narrative is internally consistent and the attributed mechanism is plausible, but the current statistical framing does not fully rule out wafer-level batch effects as an alternative explanation for part or all of the observed difference.

major comments (3)
  1. [Section IV A, Table I, Appendix C] The central statistical claim (2x improvement, statistically significant) is based on a Tukey-Kramer HSD test that treats each measured qubit as an independent observation, but the process conditions being compared are applied at wafer scale before dicing. The manuscript does not report the number of wafers per process split. If each split is represented by only one or two wafers, then wafer-to-wafer variability in Jc targeting, oxidation conditions, evaporator state, or handling is fully confounded with the treatment, and the qubit-level p-values substantially overstate the significance of the treatment effect. Please report the number of wafers per split and either perform a wafer-level or mixed-effects analysis, or provide evidence (e.g., between-wafer scatter within a split) that wafer-to-wafer variability is negligible relative to the observed 2x effect.
  2. [Section IV A] The exclusion of qubits from the T1 dataset is acknowledged, but the manuscript states that low-Jc frequency targeting and calibration failures were 'more common on wafers with pre-JJ deposition surface treatments.' Since the excluded fraction is higher in treated splits (e.g., 6/48 in Process E, larger fractions in HF splits), and since low-Jc or miscalibrated qubits could plausibly have different (typically worse) coherence properties, the reported medians may be biased upward. The sentence 'we have no evidence that this is correlated with the coherence improvements' is not a substitute for a quantitative sensitivity analysis. Please provide per-wafer exclusion counts, the frequency distributions of excluded qubits, and a robustness check (e.g., reassigning excluded qubits to low percentiles) to show that the 2x median improvement survives reasonable worst-case assumptions.
  3. [Table C.1 and Table I] The Tukey-Kramer HSD test compares group means, while Table I reports medians and quartiles. With skewed T1/Q distributions, a statistically significant difference in means does not directly establish the claimed difference in medians, and the p-values do not quantify the 2x median improvement that is the headline claim. Please either report mean-based comparisons alongside the medians or replace the test with a nonparametric comparison (e.g., Wilcoxon/Mann-Whitney) and state unambiguously whether the '2x improvement' statement refers to medians or means.
minor comments (5)
  1. [Abstract and Section II] The abstract states the surface treatments are applied 'with no other changes to the overall process flow,' but the paper actually varies the presence and size of strap connections across splits. Please qualify this statement to avoid overstating the controlled nature of the comparison.
  2. [Table I] The footnote for 'Qubits Measured' is defined only in Section IV A; including a brief definition in the table caption would improve readability.
  3. [Appendix C] The sentence 'P-values of less than 0.05 represent a rejection of the null hypothesis of equal means' is repeated in the caption; please trim redundancy and state the test assumptions explicitly.
  4. [Section IV B, Figure 6] The EELS discussion correctly notes that Ga contamination can produce a false Ge signal, but the statement that the Ge signal in Figure 6b is genuine because 'no corresponding Ga signal is present at that position' would benefit from showing the Ga profile in the same figure or panel for direct comparison.
  5. [Section III] The paper does not report the number of cooldowns or refrigerators used for each process split. Since the dataset was acquired over multiple cooldowns and four test setups, a sentence on whether process splits were balanced across measurement setups would help rule out systematic measurement drift as a confound.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the reported coherence improvement is a direct empirical comparison of measured qubit quality factors across process splits, with no fitted parameter, self-citation chain, or definitional identity producing the result.

full rationale

The paper makes no predictive or first-principles derivation that could collapse into its inputs. The central quantity, Q = omega_q * T1, is a direct definition of the measured quality factor, and T1 is measured with standard pulse-probe techniques; the comparison between Process E and POR is an empirical comparison of measured distributions, not a fitted prediction. The statistical test (Tukey-Kramer HSD) is applied to the measured qubit-level quality factors, and the p-values in Table C.1 are computed from those measurements rather than from any model parameter fitted to the outcome. The materials characterization (STEM, EDS, EELS, SEM) is independent of the qubit measurements and is used to propose a mechanism, not to define the reported improvement. No load-bearing result is justified solely by a self-citation: the cited prior work on Ge-based resist stacks [32], high-coherence qubits [4,33], and strap connections [28] provides fabrication context and expected effects, but the improvement claim rests on the in-paper measurements. The acknowledged exclusions of some qubits from the T1 dataset and the possibility of wafer-level confounding are data-interpretation and statistical-replication concerns, not circularity: they do not make the reported median equal to an input by construction. The paper is therefore self-contained with respect to the claimed empirical comparison, and no circular step is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No new physical entities or fitted parameters are introduced. The central result is empirical; the only modeling assumption is the frequency independence of Q, and no numbers are fitted to data to produce the main conclusion.

assumptions (3)
  • domain assumption Q = omega_q * T1 is frequency-independent across the 3-3.5 GHz qubit range and reflects the underlying average material loss tangent.
    Stated in Section IV A: 'we assume that these, and therefore this Q metric, are frequency independent.' This assumption underpins comparing qubits at different frequencies.
  • domain assumption Interface dielectric losses, modeled as two-level systems, dominate transmon decoherence in the device geometry studied.
    Invoked in the Introduction and Section III to motivate the study; supported by cited prior work but not separately proven here.
  • standard math The Tukey-Kramer HSD test assumptions of approximate normality and variance homogeneity hold for the quality-factor distributions.
    Used in Appendix C to compute the p-values in Table C.1; no distribution diagnostics are reported.

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Cite this review

Pith. "Pith review of Improving Transmon Qubit Performance with Fluorine-based Surface Treatments." pith.science (2026). https://pith.science/paper/TJ3KXACV

@misc{pith2026250708089,
  author       = {Pith},
  title        = {Pith review of: Improving Transmon Qubit Performance with Fluorine-based Surface Treatments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TJ3KXACV}},
  note         = {Machine review of arXiv:2507.08089}
}
abstract

Reducing materials and processing-induced decoherence is critical to the development of utility-scale quantum processors based on superconducting qubits. Here we report on the impact of two fluorine-based wet etches, which we use to treat the silicon surface underneath the Josephson junctions (JJs) of fixed-frequency transmon qubits made with aluminum base metallization. Using several materials analysis techniques, we demonstrate that these surface treatments can remove germanium residue introduced by our JJ fabrication with no other changes to the overall process flow. These surface treatments result in significantly improved energy relaxation times for the highest performing process, with median $T_1=334~\mu$s, corresponding to quality factor $Q=6.6\times10^6$. This result suggests that the metal-substrate interface directly underneath the JJs was a major contributor to microwave loss in these transmon qubit circuits prior to integration of these surface treatments. Furthermore, this work illustrates how materials analysis can be used in conjunction with quantum device performance metrics to improve performance in superconducting qubits.

Figures

Figures reproduced from arXiv: 2507.08089 by the authors.

Figure 1
Figure 1. FIG. 1. Dielectric interfaces that contribute to energy loss in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. JJ fabrication process specific to this work. (a) A tri-layer resist stack of MMA, Ge, and ZEP is prepared on patterned [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Characterization of coherence times of a qubit fabri [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Summary of coherence times across all surface treat [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. BF STEM images (top) and EDS Oxygen maps (bot [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. ADF STEM images and EELS signal profiles from the [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Top-down SEM images of JJ shadow masks with [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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