Characterization of Josephson Junction Aging and Annealing Under Different Environments

arXiv:2602.23888 · quant-ph, cond-mat.supr-con · Submitted 2026-02-27 · Read on arXiv

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Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: Today's paper: "Characterization of Josephson Junction Aging and Annealing Under Different Environments".

Mira: This study investigates the aging behavior and annealing effects on Al/AlOx/Al Josephson junctions under various storage environments,

Kai: First, who's behind it and why it matters.

Title and authors: Kai: So we're looking at this paper titled "Characterization of Josephson Junction Aging and Annealing Under Different Environments," which sounds really focused on how these junctions behave when you change where you keep them. Mira, what's your initial take on the title itself?

Mira: I see it focusing on both aging and annealing under different environments, which suggests they're trying to map out a clear relationship between external storage conditions and the resulting resistance changes in these Al/AlOx/Al junctions. It seems like they are setting up a framework to understand how we can control these devices later on.

Lev: From an error correction standpoint, I'm curious about what kind of physical systems they managed to build and measure, specifically what the experimental setup looked like for this research.

Kai: Well, the paper details that they used test Al/AlOx/Al junctions fabricated using the Dolan junction fabrication procedure that is common in superconducting qubit fabrication. They made chips about two hundred fifty nanometers by two hundred fifty nanometers and each chip had sixteen junctions, with high-resistivity silicon wafers diced into individual squares.

Mira: That's a pretty concrete description of the hardware, Kai; it tells us exactly what kind of physical scale we're dealing with when we talk about these aging dynamics.

Lev: And did they manage to get enough data points across different conditions to make meaningful comparisons between ambient, nitrogen, and vacuum environments? That's usually where you find the real challenges in simulating real-world hardware constraints.

Kai: They did exactly that, testing them from immediately after fabrication up to two or three months, and they compared chips stored in ambient laboratory conditions against those kept in a nitrogen atmosphere or a vacuum.

The paper's summary: Kai: So, the core finding is that the aging curve for these junctions follows a logarithmic pattern, and this paper explains that the overall aging amplitude is mostly controlled by how they were fabricated, but the speed at which it happens depends on where they were stored.

Mira: That logarithmic curve implies a specific mathematical relationship between time and resistance drift, which is interesting because it gives us a predictable way to model this degradation process. They've established that the aging speed is mainly dictated by the storage environment, while fabrication conditions set the amplitude of that effect.

Lev: If the speed is environmental, then for someone building real quantum hardware, that means our storage protocol needs to be extremely precise if we want stable qubit frequencies. How does this translate to running actual error correction codes on these chips?

Kai: The paper notes that ambient conditions cause the fastest aging compared to nitrogen or vacuum storage, and it even found that switching between environments can change the apparent aging speed; for instance, moving from ambient atmosphere to a glove box resulted in an observed resistance decrease, which they call junction "deaging."

Mira: That observation about deaging when swapping storage environments is significant because it shows that the environment isn't just passively degrading things; it actively influences the internal state of the junction in a way that can be manipulated by changing conditions.

Lev: So, if we can induce a temporary deaging effect by changing storage, does that give us any hope for dynamic recalibration or resetting during operation?

Kai: The paper suggests that understanding these dynamics allows researchers to plan optimal storage and annealing protocols to hit specific resistance targets, which is really practical advice for designing the hardware.

The paper's improvements: Mira: They propose using a phenomenological fit—Equation one—to describe the aging curve, which is a solid mathematical tool for modeling this behavior in terms of time and the parameters a, tau, and b. It formalizes how we quantify the relationship between fabrication quality and environmental sensitivity.

Kai: Beyond just fitting the data, they introduce a microscopic interpretation using Equation three which separates resistance drift into an intrinsic component that is environment-insensitive from one that is coupled to the environment. This helps us understand what's happening at the atomic level in terms of barrier stoichiometry and defect density.

Lev: That separation between intrinsic and extrinsic effects is huge for error correction because it tells us if we are fighting a material flaw or just environmental noise, which dictates our strategy for error correction overhead. How does this microscopic view affect the feasibility of running algorithms?

Kai: By separating these terms, they showed that the variation in amplitude between different fabrication runs is consistent with the intrinsic part, while the varying tau values are captured by tau ext(E), which they link to lowering oxygen and/or water chemical potential.

Mira: That connection to chemical potentials makes perfect sense from a materials science perspective; it directly links the physical environment—like gas purity—to the kinetic timescale of the degradation process. It grounds the model in chemistry rather than just abstract physics.

Lev: So, if we can quantify tau ext(E), does that give us any predictive power for designing materials that are inherently more robust against environmental fluctuations in a quantum setting?

Conclusion: Kai: To wrap up the paper "Characterization of Josephson Junction Aging and Annealing Under Different Environments," the main points are that aging amplitude is fabrication-dependent, aging speed is environment-dependent, and that switching storage conditions can temporarily induce deaging.

Mira: The implications suggest that for building reliable quantum processors, we absolutely must choose our storage environment very carefully because it directly controls how fast our components degrade and what resistance targets we can expect to hit.

Lev: From a hardware perspective, the paper suggests that if we want to manage drift proactively, we need to use these environmental switching techniques smartly to potentially reset the junction state before critical errors accumulate during computation.

Kai: And remember, the study also found that voltage annealing doesn't just speed up aging but actually reconfigures the internal structure of those junctions, which is a different kind of manipulation than just waiting for things to happen naturally.

Mira: It really highlights how complex these coupled relaxation processes are; we're not dealing with a single simple decay, but rather interplay between material properties and external chemical influences.

Lev: Overall, this work provides the necessary kinetic roadmap for designing storage protocols that minimize drift during long qubit operations and sets a baseline for how we model device reliability under realistic conditions.

Centre for Quantum Technologies, National University of Singapore · Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University

quant-ph, cond-mat.supr-con

Submitted: 2026-02-27

Updated: 2026-09-30

Comments: 10 pages, 4 figures. Accepted to Superconductor Science and Technology

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 77/100

The gist: This study investigates the aging behavior and annealing effects on Al/AlOx/Al Josephson junctions under various storage environments, which is critical for building large-scale superconducting

Key concepts

Junction Aging Curve: The resistance of the Josephson junction changes over time following a logarithmic pattern, described by an equation. This curve helps researchers predict how long it will take for the junction's resistance to drift and what factors control that change.
Aging Speed (τ): This parameter dictates how quickly a junction ages, meaning how fast its resistance changes over time. The study found this speed is primarily controlled by the storage environment; ambient conditions cause the fastest aging, while vacuum causes the slowest.
Annealing Effects: Applying heat or voltage to junctions can alter their properties. Thermal annealing in nitrogen environments caused resistance decreases up to 250°C, contrasting with ambient conditions where resistance behavior was more complex.
Intrinsic vs. Environment-Coupled Component: The microscopic model separates the resistance drift into two parts: an intrinsic component that is stable regardless of the environment, and an environment-coupled component that changes based on storage conditions like oxygen levels.

Terminology

Accuracy — 2× in this paper · explained in 1 episode
Annealing Effects — 2× in this paper · explained in 1 episode
Aging Speed (τ) — 1× in this paper · explained in 1 episode
Benchmark — 1× in this paper · explained in 2 episodes
Core — 1× in this paper · explained in 2 episodes
Quantum Circuits — 1× in this paper · explained in 2 episodes

Summary

This study investigates the aging behavior and annealing effects on Al/AlOx/Al Josephson junctions under various storage environments, which is critical for building large-scale superconducting quantum processors where precise frequency assignment is necessary. The research demonstrates that junction aging follows a logarithmic curve, with the aging amplitude primarily dictated by fabrication conditions and the aging speed determined by storage conditions. Understanding these dynamics allows for planning optimal storage and annealing protocols to achieve desired resistance targets.

Junction Aging Behavior Under Different Storage Conditions

The researchers studied the effects of aging on Al/AlOx/Al junctions over several months in three primary environments: ambient laboratory conditions, a nitrogen atmosphere (glove box), and vacuum. The findings indicate that the aging speed is mainly dependent on the storage conditions, with ambient conditions resulted in fastest aging compared to junctions stored in a nitrogen atmosphere or vacuum. Conversely, the aging amplitude appears to be limited mainly by fabrication conditions. Furthermore, changing storage conditions can cause an apparent change in aging speed; specifically, a resistance decrease (≡ junction “deaging”) observed when the junctions are moved from ambient atmosphere to glove box. The aging curve was phenomenologically fitted using the equation:

R(t ≈ 0) = 1 + a log t τ + b (Equation 1).

Effect of Storage Environment on Aging Speed and Amplitude

The analysis revealed distinct differences in aging kinetics across environments. The aging speed (corresponding to τ) mainly depends on the storage condition, with ambient conditions being about 3 to 4 times faster than in nitrogen environment, and about 5 times faster than in vacuum. The aging amplitude (a) was found to be limited by fabrication conditions, as evidenced by the comparison between Chip 1 (ambient) and Chip 2 (glove box), where the amplitude of the aging varied depending on the chip/fabrication. The storage environment also influences which relaxation bounds are approached. For example, in alternating environment chips, swapping storage conditions resulted in a significant change in apparent aging speed, with When the chips were moved from glove box to atmosphere, the chips aged more rapidly.

Thermal and Voltage Annealing Effects

The study compared the effect of thermal annealing under different conditions against ambient annealing up to 250°C. The results showed that under a nitrogen environment, the resistances decreased at all temperatures tested, while in an ambient environment, the resistances increased at 200◦C and decreased at 250◦C instead. For voltage annealing using the alternating bias assisted annealing (ABAA) method, the process did not induce accelerated aging but instead changed the internal structure of the junctions. Specifically, annealed junctions on both chips showed an average resistance increase of approximately 7.5 komega for Chip 1 and about 18% increase for Chip 2.

Microscopic Interpretation of Resistance Drift

The resistance drift is interpreted through a coupled microscopic model described by Equation 3:

RN (t) / R0 = 1 + aint log(1 + t / τint) + aext log(1 + t / τext(E)). This framework separates the intrinsic (environment-insensitive) component from the environment-coupled component. The separation of fitted τ values between ambient, nitrogen, and vacuum conditions is captured by τext(E), which is suppressed by lowering oxygen and/or water chemical potential. The variation in amplitude (a) between fabrication runs is consistent with aint,ext being set by the as-fabricated distribution of barrier stoichiometry, defect density, and local transparency inhomogeneity.

Conclusion on Optimal Storage

The research concludes that to control junction aging effectively, careful consideration of storage conditions is paramount. While high-vacuum storage resulted in the slowest aging, the most optimal storage condition appears to be N2 glove box due to comparable aging speed. The study also noted that Uncontrolled storage between qubit junction resistance measurement and cooldown can result in unexpected qubit frequency shift beyond the desired range. Furthermore, voltage annealing acts as a perturbation primarily on the internal reservoir, effectively reconfiguring conduction paths rather than simply accelerating environmental aging mechanisms. Finally, for thermal annealing in ambient conditions, a competition between deaging and aging processes was observed.

Key Findings Summary:

Aging amplitude is mainly determined by fabrication conditions.
Aging speed (τ) is mainly determined by storage conditions (ambient > N2 > vacuum).
Moving from ambient to glove box can cause an initial deaging.
Voltage annealing changes the internal configuration of junctions, not just accelerating aging.
Thermal annealing in N2 leads to resistance decrease up to 250°C, while in ambient conditions shows a complex temperature dependence (increase at 200°C, decrease at 250°C).
The minimum resistance tuning range is limited by the initial resistance value.

Improvements for AI systems

Here are the specific improvements that could be made to AI systems, leveraging the scientific insights from this paper:

The core improvement lies in developing AI models capable of predicting and controlling physical system dynamics governed by complex, coupled relaxation processes (like those described by Eq. 3).

The ability to model and predict Aging dynamics based on environmental coupling:
The ability to optimize Annealing strategies for precise resistance tuning:

Specific Improvements and Capabilities:

Predictive Modeling of Environmental Sensitivity in Quantum Devices:

A machine learning model (e.g., a Graph Neural Network or a specialized Physics-Informed Neural Network) should be trained on the parameters derived from Equation 3, specifically mapping the input storage environment vector (O2 concentration, temperature, humidity) and fabrication parameters to the predicted aging speed parameter, τext(E).

The improved AI system can predict how rapidly a Josephson junction's resistance will drift under specific storage conditions (e.g., predicting if a chip stored in ambient air will age 3x faster than one in N2) before physical measurement is even performed.

Optimization of Dynamic Annealing Protocols:

An Reinforcement Learning (RL) agent should be developed to control voltage or thermal annealing sequences based on the predicted internal state changes (the internal reservoir parameters, aint and R0).

The improved AI system can autonomously determine the optimal sequence of alternating bias pulses or temperature steps required to achieve a specific target resistance change while minimizing detrimental effects like accelerated aging or irreversible damage. It would learn to distinguish between perturbations that modify the internal configuration versus those that simply accelerate environmental aging.

Deconvolution of Intrinsic vs. Extrinsic Physical Effects:

A Bayesian inference framework can be employed to continuously estimate the relative contributions of the intrinsic relaxation timescale (τint) and the environment-coupled timescale (τext(E)) in real-time from resistance drift data.

The improved AI system can disentangle whether a measured change in resistance is due to inherent material defects (fabrication quality, aint) or external factors like oxygen exchange with the environment. This allows for more accurate diagnosis of device failure modes and better understanding of the underlying physical mechanism causing observed degradation.

Predictive Deaging Thresholds:

The AI system can be trained to identify the transition points where temporary deaging (observed during environment swapping) occurs, allowing it to predict the optimal timing for intervention before irreversible aging sets in.

This capability enables proactive management of qubit performance by suggesting when to move a chip from one storage condition to another to temporarily reverse its drift trajectory.