Polar codes and its implementation challenges are an active study field since [1]. SC decoding algorithm is the well known polar decoding algortihm described in [1]. The chip area of the SC decoder is dominated by memory and register chains in the deeply-pipelined architecture [2]. An adaptive quantization technique is proposed to reduce the memory complexity of the polar SC decoder. Due to polarization of synthesized bit channels, some portion of the bits could be decoded by diminishing the log-likelihood ratios' (LLRs) bit precision with a negligible communications performance loss. Unlike standard SC decoding, which operates with constant bit precision throughout decoding, this method utilizes varying number of bits to represent LLRs among the levels of the decoder. Bit precision of each decoding level could be determined by a heuristic method. Another method is motivated by maximizing the mutual information between the levels of the decoder [3]. Adaptive quantization method reduces the memory complexity of the decoder proportional to coding rate.