Abstract:
Numerous examples are disclosed for an output block coupled to a non-volatile memory array in a neural network and associated methods. In one example, a circuit for converting a current in a neural network into an output voltage comprises a non-volatile memory cell comprises a word line terminal, a bit line terminal, and a source line terminal, wherein the bit line terminal receives the current; and a switch for selectively coupling the word line terminal to the bit line terminal; wherein when the switch is closed, the current flows into the non-volatile memory cell and the output voltage is provided on the bit line terminal.
Abstract:
Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. In one example, a method is disclosed of testing a plurality of non-volatile memory cells in an array of non-volatile memory cells, wherein the array is arranged in rows and columns, wherein each row is coupled to a word line and each column is coupled to a bit line, and wherein each word line is selectively coupled to a row decoder and each bit line is selectively coupled to a column decoder, the method comprising asserting, by the row decoder, all word lines in the array; asserting, by the column decoder, all bit lines in the array; performing a deep programming operation on the array of non-volatile memory cells; and measuring a total current received from the bit lines.
Abstract:
A method of forming a memory device includes forming a second insulation layer on a first conductive layer formed on a first insulation layer formed on semiconductor substrate. A trench is formed into the second insulation layer extending down and exposing a portion of the first conductive layer, which is etched or oxidized to have a concave upper surface. Two insulation spacers are formed along sidewalls of the trench, having inner surfaces facing each other and outer surfaces facing away from each other. A source region is formed in the substrate between the insulation spacers. The second insulation layer and portions of the first conductive layer are removed to form floating gates under the insulation spacers. A third insulation layer is formed on side surfaces of the floating gates. Two conductive spacers are formed along the outer surfaces. Drain regions are formed in the substrate adjacent the conductive spacers.
Abstract:
In one example, a non-volatile memory system comprises an array of non-volatile memory cells arranged in rows and columns, each non-volatile memory cell comprising a source and a drain; a plurality of bit lines, each of the plurality of bit lines coupled to the drain of each non-volatile memory cell in a column of non-volatile memory cells; a source line coupled to the source of each non-volatile memory cell; and an adaptive bias decoder for providing a voltage to a word line of the array during an operation, wherein the adaptive bias decoder adjusts the voltage provided to the word line in response to changes in a voltage of the source line.
Abstract:
An artificial neural network device that utilizes one or more non-volatile memory arrays as the synapses. The synapses are configured to receive inputs and to generate therefrom outputs. Neurons are configured to receive the outputs. The synapses include a plurality of memory cells, wherein each of the memory cells includes spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate disposed over and insulated from a first portion of the channel region and a non-floating gate disposed over and insulated from a second portion of the channel region. Each of the plurality of memory cells is configured to store a weight value corresponding to a number of electrons on the floating gate. The plurality of memory cells are configured to multiply the inputs by the stored weight values to generate the outputs. Various algorithms for tuning the memory cells to contain the correct weight values are disclosed.
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 method of forming a memory device by forming spaced apart first and second regions with a channel region therebetween, forming a floating gate over and insulated from a first portion of the channel region, forming a control gate over and insulated from the floating gate, forming an erase gate over and insulated from the first region, and forming a select gate over and insulated from a second portion of the channel region. Forming of the floating gate includes forming a first insulation layer on the substrate, forming a first conductive layer on the first insulation layer, and performing two separate etches to form first and second trenches through the first conductive layer. A sidewall of the first conductive layer at the first trench has a negative slope and a sidewall of the first conductive layer at the second trench is vertical.
Abstract:
In one example, a method comprises determining a logarithmic slope factor for a selected analog non-volatile memory cell in an array of analog non-volatile memory cells while the selected analog non-volatile memory cell is operating in a sub-threshold region; storing the logarithmic slope factor; determining a linear slope factor for the selected analog non-volatile memory cell while the selected analog non-volatile memory cell is operating in a linear region; storing the linear slope factor; and utilizing one or more of the logarithmic slope factor and the linear slope factor when programming the selected analog non-volatile memory cell to a target current.
Abstract:
A simplified method for forming pairs of non-volatile memory cells using two polysilicon depositions. A first polysilicon layer is formed on and insulated from the semiconductor substrate in a first polysilicon deposition process. A pair of spaced apart insulation blocks are formed on the first polysilicon layer. Exposed portions of the first poly silicon layer are removed while maintaining a pair of polysilicon blocks of the first polysilicon layer each disposed under one of the pair of insulation blocks. A second polysilicon layer is formed over the substrate and the pair of insulation blocks in a second polysilicon deposition process. Portions of the second polysilicon layer are removed while maintaining a first polysilicon block (disposed between the pair of insulation blocks), a second polysilicon block (disposed adjacent an outer side of one insulation block), and a third polysilicon block (disposed adjacent an outer side of the other insulation block).
Abstract:
Testing circuitry and methods are disclosed for use with analog neural memory in deep learning artificial neural networks. In one example, a method comprises programming an analog neural non-volatile memory cell in an array to a target value representing one of N different values, where N is an integer; verifying that a value stored in the analog neural non-volatile memory cell is within an acceptable window of values around the target value; repeating the programming and verifying for each of the N values; and identifying the analog neural non-volatile memory cell as bad if any of the verifying indicates a value stored in the cell outside of the acceptable window of values around the target value.