Oxide-nitride heteroepitaxy for low-loss dielectrics in superconducting quantum circuits

  1. David A. Garcia-Wetten,
  2. Mitchell J. Walker,
  3. Peter G. Lim,
  4. André Vallières,
  5. Maria G. Jimenez-Guillermo,
  6. Miguel A. Alvarado,
  7. Dominic P. Goronzy,
  8. Anna Grassellino,
  9. Jens Koch,
  10. Vinayak P. Dravid,
  11. Mark C. Hersam,
  12. and Michael J. Bedzyk
Superconducting qubits show great promise for the realization of fault-tolerant quantum computing, but lossy, amorphous dielectrics limit current technology. Identifying highly crystalline
and stoichiometric dielectrics with intrinsically low microwave loss is therefore a central materials challenge, yet experimentally validated platforms remain scarce. In this work, we integrate a crystalline dielectric into a heteroepitaxial TiN/γ-Al2O3/TiN trilayer grown via pulsed laser deposition. Correlative high-resolution imaging, diffraction, and spectroscopy measurements confirm the single-crystal quality and chemical integrity of all layers, with minimal defects and limited anion interdiffusion across the oxide-nitride interfaces. Using microwave lumped-element resonators with parallel-plate capacitors, we report the first direct measurement of the dielectric loss of epitaxial γ-Al2O3, for which we find a low intrinsic two-level system loss, δ0TLS=(2.8±0.1)×10−5. These results establish heteroepitaxial oxides on transition metal nitrides as an attractive materials platform for superconducting quantum circuits, particularly for integration into compact device architectures such as merged-element transmons and microwave kinetic inductance detectors.

Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

  1. Akshay A. Murthy,
  2. Mustafa Bal,
  3. Michael J. Bedzyk,
  4. Hilal Cansizoglu,
  5. Randall K. Chan,
  6. Venkat Chandrasekhar,
  7. Francesco Crisa,
  8. Amlan Datta,
  9. Yanpei Deng,
  10. Celeo D. Matute Diaz,
  11. Vinayak P. Dravid,
  12. David A. Garcia-Wetten,
  13. Sabrina Garattoni,
  14. Sunil Ghimire,
  15. Dominic P. Goronzy,
  16. Sebastian de Graaf,
  17. Sam Haeuser,
  18. Mark C. Hersam,
  19. Dieter Isheim,
  20. Kamal Joshi,
  21. Richard Kim,
  22. Saagar Kolachina,
  23. Cameron J. Kopas,
  24. Matthew J. Kramer,
  25. Ella O. Lachman,
  26. Jaeyel Lee,
  27. Peter G. Lim,
  28. Andrei Lunin,
  29. William Mah,
  30. Jayss Marshall,
  31. Josh Y. Mutus,
  32. Jin-Su Oh,
  33. David Olaya,
  34. David P. Pappas,
  35. Joong-mok Park,
  36. Ruslan Prozorov,
  37. Roberto dos Reis,
  38. David N. Seidman,
  39. Zuhawn Sung,
  40. Makariy Tanatar,
  41. Mitchell J. Walker,
  42. Jigang Wang,
  43. Haotian Wu,
  44. Lin Zhou,
  45. Shaojiang Zhu,
  46. Anna Grassellino,
  47. and Alexander Romanenko
The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting
transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values.