Studying the threshold behavior of surface codes under biased noise models is an active area of research. Previous work Tuckett et al. (2018), using an optimal tensor-network decoder, demonstrated that replacing Z-type stabilizers with Y-type stab
Studying the threshold behavior of surface codes under biased noise models is an active area of research. Previous work Tuckett et al. (2018), using an optimal tensor-network decoder, demonstrated that replacing Z-type stabilizers with Y-type stabilizers significantly improves the surface code threshold under code-capacity level dephasing noise. In this work, we construct and study a ZY surface code by replacing the X-type stabilizers with Y-type stabilizers. We compare it with the standard ZX surface code under circuit-level Pauli-X biased noise, with and without an additional gate-based XX crosstalk noise. We find that for the ZX surface code, the X-memory threshold increases monotonically with bias while the Z-memory threshold decreases and saturates. For the ZY surface code, the Y-memory threshold is nearly constant across all bias values. The Z-memory thresholds of the ZX and ZY codes are consistent within the uncertainty. Adding XX crosstalk reduces the Z-memory threshold beyond the fitting uncertainty while leaving the X memory threshold largely unaffected. The choice of CNOT ordering redistributes threshold performance between the two logical memories. Our work extends prior observations from code-capacity level noise to circuit-level noise. It also indicates the need for decoders capable of jointly reasoning over correlated syndrome information so that tailored stabilizer structures could be fully utilized for quantum error correction.