Abstract:
A non-volatile memory device and method for programming cells using repeated pulses of program voltages, with interleaved read operations to determine the level of read current, until the desired programming state is achieved. Each successive program pulse has one or more program voltages increased by a step value relative to the previous pulse. For a single level cell type, each cell is individually removed from the programming pulses after reaching a first read current threshold, and the step value is increased for one or more kicker pulses thereafter. For a multi-level cell type, the step value drops after one of the cells reaches a first read current threshold, some cells are individually removed from the programming pulses after reaching a second read current threshold while others are individually removed from the programming pulses after reaching a third read current threshold.
Abstract:
A memory device array with spaced apart parallel isolation regions formed in a semiconductor substrate, with an active region between each pair of adjacent isolation regions. Each isolation region includes a trench formed into the substrate surface and an insulation material formed in the trench. Portions of a top surface of the insulation material are recessed below the surface of the substrate. Each active region includes a column of memory cells each having spaced apart first and second regions with a channel region therebetween, a floating gate over a first channel region portion, and a select gate over a second channel region portion. The select gates are formed as continuous word lines extending perpendicular to the isolation regions and each forming the select gates for one row of the memory cells. Portions of each word line extend down into the trenches and disposed laterally adjacent to sidewalls of the trenches.
Abstract:
A method of CMP planarizing a silicon dioxide layer on a silicon nitride in the semiconductor die is disclosed. A wafer has a plurality substantially identical semiconductor dies defined on the wafer. Each of the dies is separated from one another by a scribe line. A layer of silicon nitride is formed on the planar surface of the wafer where the silicon nitride has a top surface which is substantially parallel to the planar surface. A layer of silicon dioxide is deposited on the top surface with the silicon dioxide varying in height above the top surface. A mask is formed across the wafer, including on the scribe line, where the mask has a plurality of locations with each location having a differing density of gap-to-pillar ratio, which is proportional to the height of the silicon dioxide above the top surface. The silicon dioxide is anisotropically etched through each gap of the mask across the entire wafer where each gap is etched by the same amount in the height direction. CMP is then used to planarize the silicon dioxide to the top surface of the silicon nitride across the entire wafer.
Abstract:
In one example, a system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells arranged in rows and columns; and a sigma-delta analog-to-digital converter to receive a current from a column of the vector-by-matrix multiplication array and to generate a digital output in response to the current.
Abstract:
Various examples of decoders and physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. In one example, a system comprises a plurality of vector-by-matrix multiplication arrays in an analog neural memory system, each vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a word line terminal; a plurality of read row decoders, each read row decoder coupled to one of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows during a read operation; and a shared program row decoder coupled to all of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows in one or more of the vector-by-matrix multiplication arrays during a program operation.
Abstract:
Numerous embodiments for improving an analog neural memory in a deep learning artificial neural network as to accuracy or power consumption as temperature changes are disclosed. In some embodiments, a method is performed to determine in real-time a bias value to apply to one or more memory cells in a neural network. In other embodiments, a bias voltage is determined from a lookup table and is applied to a terminal of a memory cell during a read operation.
Abstract:
In one example, a circuit comprises an input transistor comprising a first terminal, a second terminal coupled to ground, and a gate; a capacitor comprising a first terminal and a second terminal; an output transistor comprising a first terminal providing an output current, a second terminal coupled to ground, and a gate; a first switch; and a second switch; wherein in a first mode, the first switch is closed and couples an input current to the first terminal of the input transistor and the gate of the input transistor and the second switch is closed and couples the first terminal of the input transistor to the first terminal of the capacitor and the gate of the output transistor, and in a second mode, the first switch is open and the second switch is open and the capacitor discharges into the gate of the output transistor.
Abstract:
A memory cell array having rows and columns of memory cells with respective ones of the memory cells including spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate over a first portion of the channel region, a select gate over a second portion of the channel region, and an erase gate over the source region. A strap region is disposed between first and second pluralities of the columns. For one memory cell row, a dummy floating gate is disposed in the strap region, an erase gate line electrically connects together the erase gates of the memory cells in the one row and in the first plurality of columns, wherein the erase gate line is aligned with the dummy floating gate with a row direction gap between the erase gate line and the dummy floating gate.
Abstract:
In one example disclosed herein, a system comprises an analog computation-in-memory engine to perform operations in a first layer in a neural network and a digital computation-in-memory engine to perform operations in a second layer different than the first layer in the neural network. The system optionally comprises a dynamic weight engine to perform operations in a third layer different than the first layer and the second layer in the neural network.