exists on the error characteristics considers the flash memory endurance [1; 21; 22; 23], i.e., the number of program/erase (P/E) cycles. Over time, charge losses occur, which cause a change of the threshold voltage distribution [24]. In many applications, data retention time is more important than flash endurance. Likewise, read disturb, inter-cell interference, and temperature related charge losses occur [25; 26; 27; 28]. Such charge losses result in shifts of the threshold voltages. These voltage shifts increase the bit error probability if the reference voltages are not adapted to the actual life cycle conditions. Compared with hard-input read operation, the threshold voltage sensing operation to obtain the soft information causes a larger latency and a higher energy consumption [29]. Hence, the soft-input decoding should only be used for blocks where the hard-input decoding fails. Algorithms that adapt the read references can reduce the latency and energy consumption by avoiding soft-reads. Moreover, many soft-input decoding approaches assume reading procedures that maximize the mutual information (MI) between the input and output of flash memory. This requires an accurate adaptation of the read voltages and additional estimation of log-likelihood ratios (LLR) for the quantization intervals [15; 22]. Several publications proposed threshold adaptation concepts [25; 30; 31; 32; 33; 34; 35]. These approaches adjust the read references to minimize bit error rates. For instance, the flash controller may apply a read-retry mechanism to adapt the reference voltages. Firstly, the controller reads the data with the default read reference. If the ECC decoder corrects the errors, there is no need to adjust the default value. In the case of decoding failures, the flash controller can perform repeated reads with certain threshold voltages until all errors are corrected by the decoder. However, such read-retry mechanisms can be very time-consuming. To obtain statistical information about the cell state, a sensing directed estimation method was proposed in [31]. This method approximates the threshold voltage distribution as a Gaussian mixture. It estimates the mean and standard deviation values based on measured distributions. This scheme does not utilize any known pilot bits but employs extra memory sensing operations similar to the read-retry mechanisms. Such voltage sensing operations incur a larger energy consumption and a latency penalty. Similarly, a parameter estimation approach and a threshold adaptation policy is derived in [33]. This adaptation method is also based on the assumption of Gaussian voltage distributions. However, the cell threshold voltage distributions of MLC and TLC are actually highly asymmetric with exponential tails [23; 36; 37] and cannot be estimated with Gaussian distribution. In [25; 30], coding methods based on Berger codes are suggested that exploit the asymmetric error probabilities of the channel. Berger codes are used for error detection. They can detect any number of one-to-zero errors, as long as no zero-to-one errors occurred in the same codeword. Likewise, Berger codes can detect any number of zero-to-one errors, as long as no one-to-zero bit-flip errors occur in the same code word. However, Berger codes cannot correct any errors. In this work, we propose two methods for reference calibration that aim on minimizing the BER. Similar to the methods proposed in [32; 34], we exploit the error correction code to obtain information about the cell state. Based on the number of observed errors in the read codeword, we predict the shift of the reference voltages. It is demonstrated that the observed number of errors is a good feature for the voltage shifts and is utilized for the initial calibration of the read thresholds. However, the total number of errors is only a good measure if there is a relatively large difference between the threshold for the first readout and the optimal read threshold.