Abstract: A solid-state storage retention monitor determines whether user data in a solid-state device is in need of a scrubbing operation. One or more reference blocks may be programmed with a known data pattern, wherein the reference block(s) experiences substantially similar P/E cycling, storage temperature, storage time, and other conditions as the user blocks. The reference blocks may therefore effectively represent data retention properties of the user blocks and provide information regarding whether/when a data refreshing operation is needed.

Abstract: A programming technique which reduces program disturb in a non-volatile storage system is disclosed. A positive voltage may be applied to a substrate (e.g., p-well) during programming. Biasing the substrate may improve boosting of channels of unselected NAND strings, which may reduce program disturb. The substrate may be charged up during the programming operation, and discharged after programming. Therefore, for operations such as verify and read, the substrate may be grounded. In one embodiment, the substrate is charged just prior to applying a program pulse, then discharged prior to a program verify operation. In one embodiment, the substrate is charged while unselected word lines are ramped up to a pass voltage. The substrate bias may depend on program voltage, temperature, and/or hot count.

Abstract: A set of reliability metrics is provided for use by an iterative probabilistic decoding process for non-volatile storage. A plurality of sense operations are performed on at least one set of non-volatile storage elements which are programmed to a plurality of programming states. A set of reliability metrics such as logarithmic likelihood ratios is provided based on the sense operations. The set of reliability metrics is can be used by an iterative probabilistic decoding process in determining a programming state of at least one non-volatile storage element based on at least one subsequent sense operation involving the at least one non-volatile storage element. The plurality of sense operations can be performed at different ages (e.g., number of program/erase cycles) of the at least one set of non-volatile storage elements and the set of reliability metrics can be based on an average over the different ages.

Abstract: In one embodiment, a memory device includes a substrate, a tunneling oxide, a silicide nanocrystal floating gate, and a control oxide. The tunneling oxide is positioned upon a first surface of the substrate, the silicide nanocrystal floating gate is positioned upon the tunneling oxide, and the control oxide positioned upon the nanocrystal floating gate.

Abstract: To program a set of non-volatile storage elements, a set of programming pulses are applied to the control gates (or other terminals) of the non-volatile storage elements. The programming pulses have pulse widths that vary as a function of simulated pulse magnitude data. The programming pulses can also have pulse magnitudes that vary based on measurements taken while testing the set of non-volatile storage elements. In one embodiment, the pulse widths are determined after simulation performed prior to fabrication of the non-volatile storage elements. In another embodiment, the pulse magnitudes are calculated after fabrication of the non-volatile storage elements.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding and multiple read operations to achieve greater reliability. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding read data of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. If convergence does not occur, e.g., within a set time period, the state of the non-volatile storage element is sensed again, current values of the reliability metrics in the decoder are adjusted, and the decoding again attempts to converge.

Abstract: A programming technique which reduces program disturb in a non-volatile storage system is disclosed. A positive voltage may be applied to a substrate (e.g., p-well) during programming. Biasing the substrate may improve boosting of channels of unselected NAND strings, which may reduce program disturb. The substrate may be charged up during the programming operation, and discharged after programming. Therefore, for operations such as verify and read, the substrate may be grounded. In one embodiment, the substrate is charged just prior to applying a program pulse, then discharged prior to a program verify operation. In one embodiment, the substrate is charged while unselected word lines are ramped up to a pass voltage. The substrate bias may depend on program voltage, temperature, and/or hot count.

Abstract: To program a set of non-volatile storage elements, a set of programming pulses are applied to the control gates (or other terminals) of the non-volatile storage elements. The programming pulses have pulse widths that vary as a function of simulated pulse magnitude data. The programming pulses can also have pulse magnitudes that vary based on measurements taken while testing the set of non-volatile storage elements. In one embodiment, the pulse widths are determined after simulation performed prior to fabrication of the non-volatile storage elements. In another embodiment, the pulse magnitudes are calculated after fabrication of the non-volatile storage elements.

Abstract: To program a set of non-volatile storage elements, a set of programming pulses are applied to the control gates (or other terminals) of the non-volatile storage elements. The programming pulses have pulse widths that vary as a function of simulated pulse magnitude data. The programming pulses can also have pulse magnitudes that vary based on measurements taken while testing the set of non-volatile storage elements. In one embodiment, the pulse widths are determined after simulation performed prior to fabrication of the non-volatile storage elements. In another embodiment, the pulse magnitudes are calculated after fabrication of the non-volatile storage elements.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Soft data bits are read from the memory if the decoding fails to converge. Initial reliability metric values are provided after receiving the hard read results and at each phase of the soft bit operation(s). In one embodiment, a second soft bit is read from the memory using multiple subsets of soft bit compare levels. While reading at the second subset of compare levels, decoding can be performed based on the first subset data.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Soft data bits are read from the memory if the decoding fails to converge. Initial reliability metric values are provided after receiving the hard read results and at each phase of the soft bit operation(s). In one embodiment, a second soft bit is read from the memory using multiple subsets of soft bit compare levels. While reading at the second subset of compare levels, decoding can be performed based on the first subset data.

Abstract: To program a set of non-volatile storage elements, a set of programming pulses are applied to the control gates (or other terminals) of the non-volatile storage elements. The programming pulses have pulse widths that vary as a function of simulated pulse magnitude data. The programming pulses can also have pulse magnitudes that vary based on measurements taken while testing the set of non-volatile storage elements. In one embodiment, the pulse widths are determined after simulation performed prior to fabrication of the non-volatile storage elements. In another embodiment, the pulse magnitudes are calculated after fabrication of the non-volatile storage elements.

Abstract: To program a set of non-volatile storage elements, a set of programming pulses are applied to the control gates (or other terminals) of the non-volatile storage elements. The programming pulses have pulse widths that vary as a function of simulated pulse magnitude data. The programming pulses can also have pulse magnitudes that vary based on measurements taken while testing the set of non-volatile storage elements. In one embodiment, the pulse widths are determined after simulation performed prior to fabrication of the non-volatile storage elements. In another embodiment, the pulse magnitudes are calculated after fabrication of the non-volatile storage elements.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Soft data bits are read from the memory if the decoding fails to converge. Initial reliability metric values are provided after receiving the hard read results and at each phase of the soft bit operation(s). In one embodiment, a second soft bit is read from the memory using multiple subsets of soft bit compare levels. While reading at the second subset of compare levels, decoding can be performed based on the first subset data.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Soft data bits are read from the memory if the decoding fails to converge. Initial reliability metric values are provided after receiving the hard read results and at each phase of the soft bit operation(s). In one embodiment, a second soft bit is read from the memory using multiple subsets of soft bit compare levels. While reading at the second subset of compare levels, decoding can be performed based on the first subset data.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding and multiple read operations to achieve greater reliability. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding read data of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. If convergence does not occur, e.g., within a set time period, the state of the non-volatile storage element is sensed again, current values of the reliability metrics in the decoder are adjusted, and the decoding again attempts to converge.

Abstract: Data stored in non-volatile storage is decoded using iterative probabilistic decoding and multiple read operations to achieve greater reliability. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding read data of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. If convergence does not occur, e.g., within a set time period, the state of the non-volatile storage element is sensed again, current values of the reliability metrics in the decoder are adjusted, and the decoding again attempts to converge. In another approach, the initial reliability metrics are based on multiple reads. Tables which store the reliability metrics and adjustments based on the sensed states can be prepared before decoding occurs.

Abstract: Nanocrystal memories and methods of making the same are disclosed. In one embodiment, a memory device comprises a substrate, a tunneling oxide, a silicide nanocrystal floating gate, and a control oxide. The tunneling oxide is positioned upon a first surface of the substrate, the silicide nanocrystal floating gate is positioned upon the tunneling oxide, and the control oxide positioned upon the nanocrystal floating gate.

Abstract: Data stored in non-volatile storage is read using sense operations and associated pre-conditioning waveforms. The pre-conditioning waveform provides a short term history for a non-volatile element which is analogous to the conditions experienced during programming when a programming pulse is applied prior to a verify operation. The pre-conditioning waveform can cause electrons to enter and exit trap sites, for instance, so that the accuracy of a probabilistic decoding process is improved. In one approach, multiple read operations are performed, some with pre-conditioning waveforms and some without. Pre-conditioning waveforms with different characteristics, such as amplitude, shape, duration and time before the associated read pulse, can also be used. For probabilistic decoding, initial reliability metrics can be developed based on multiple reads. Tables which store the reliability metrics can then be prepared for use in subsequent decoding.

Abstract: An array of non-volatile storage elements includes a first group of non-volatile storage elements connected to a selected word line, a second group of non-volatile storage elements connected to the selected word line, a first group of bit lines in communication with the first group of non-volatile storage elements, a second group of bit lines in communication with the second group of non-volatile storage elements, a first set of sense modules located at a first location and connected to the first group of bit lines, and a second set of sense modules located at a second location and connected to the second group of bit lines. The first set of sense modules applies a first bit line voltage based on the bit line distance between the first set of sense modules and the first group of non-volatile storage elements. The second set of sense modules applies a second bit line voltage based on the bit line distance between the second set of sense modules and the second group of non-volatile storage elements.