An indirect time-of-flight (iToF) sensor has been spotlighted for its high-quality depth images within a few meter ranges, making it a strong candidate for realizing metaverse applications in mobile devices [1]. However, one of the unresolved challenges in iToF sensors is low background light (BGL) resilience, which can saturate floating diffusion (FD) nodes and deteriorate a demodulation contrast (DC), causing large depth distortion and noise. Even though in-pixel auto-zeroing and chopping techniques have achieved BGL robustness up to 200 klx, it still has large noise of over 3% due to noise contribution from the BGL cancellation technique [2]. The in-pixel voltage subtraction scheme has been reported for compensating BGL up to 180 klx, but it requires tons of switching operations, generating large noise as well as charge injections [3]. The column-parallel $\Delta\Sigma$ BGL canceling technique has been introduced to subtract a common-mode voltage of each FD node and accumulate differential signals [4]. However, the column-parallel architecture has limited scalability and still suffers from additional reset noise due to the dedicated number of $\Delta\Sigma$-operations. Another big challenge of iToF sensor is the degradation of depth non-linearity and precision caused by FD mismatches [5]. Although common-mode voltage has been successfully compensated in [4], FD mismatches can still convert residual BGL to phase information.