Solid State Storage Device High-Speed Writing Method and System

Abstract

The present invention relates to the technical field of data processing, and particularly to a high-speed writing method and system for a solid-state storage device. The writing method includes the following steps: applying a specific pulse voltage across the two ends of the storage unit of the solid-state storage device to enable electrons to perform quantum tunneling through the insulating layer, generating quantum tunneling conditions for writing data with low power consumption; preprocessing the storage unit, and the preprocessing steps include initialization, state detection, and insulating layer repair to ensure that each storage unit is in a state suitable for writing. The high-speed writing method and system for the solid-state storage device of the present invention solve the problem that it is difficult to balance low power consumption and high stability in the high-speed writing process of the existing solid-state storage device.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically provides a high-speed writing method and system for solid-state storage devices. Background Art

[0002] With the continuous improvement of storage density and the increasing complexity of application requirements, solid-state storage devices face a series of technical challenges during high-speed writing. The writing methods of traditional solid-state storage devices mainly rely on the charge trap storage mechanism, and their writing speed and power consumption are limited by material properties and environmental conditions. Especially in high-frequency and large-data-volume writing operations, the charge accumulation in storage cells and the damage of the insulating layer will lead to increased writing latency, data loss, and shortened storage life. In addition, although multi-core parallel writing technology can improve the writing speed, it lacks effective writing parameter optimization and real-time monitoring mechanisms, and it is difficult to maintain stability and reliability in complex environments.

[0003] The present invention aims to solve the main technical problems existing in the high-speed writing process of existing solid-state storage devices: that is, how to achieve high-speed writing under low power consumption and high stability, while ensuring the integrity and reliability of data. Summary of the Invention

[0004] The present invention provides a high-speed writing method and system for solid-state storage devices to solve the technical problems of high power consumption and instability during high-speed writing of solid-state storage devices.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] On the one hand, a high-speed writing method for a solid-state storage device is provided, and the writing method includes the following steps:

[0007] Apply a specific pulse voltage across the storage cells of the solid-state storage device to enable electrons to perform quantum tunneling through the insulating layer, generating quantum tunneling conditions for low-power data writing;

[0008] Preprocess the storage cells, and the preprocessing steps include initialization, state detection, and insulating layer repair to ensure that each storage cell is in a suitable state for writing;

[0009] Determine the optimal writing parameters through an adaptive quantum tunneling algorithm, and the adaptive quantum tunneling algorithm dynamically adjusts the voltage and current according to the current state and environmental conditions of the solid-state storage device to optimize the writing speed;

[0010] Adopt parallel quantum tunneling to write multiple storage cells simultaneously, and each storage cell is independently controlled to reduce the writing latency;

[0011] Monitor and feedback the writing process in real time, detect the motion state of electrons and the physical characteristics of storage cells during the writing process through built-in sensors, and adjust the writing parameters according to the feedback results;

[0012] Use a verification algorithm to verify the data written to the solid-state storage device to ensure the integrity of the data.

[0013] On the other hand, provide a high-speed writing system for a solid-state storage device. The writing system includes:

[0014] A voltage generation module, implemented through a high-precision voltage controller, for generating a pulsed voltage under quantum tunneling conditions;

[0015] A preprocessing module, including charge balance, state detection, and insulating layer repair, for preprocessing the storage cells;

[0016] An adaptive quantum tunneling algorithm module for dynamically adjusting the writing parameters according to the current state of the storage device and environmental conditions;

[0017] A parallel writing module, which writes multiple storage cells simultaneously through a multi-core control circuit, for implementing parallel quantum tunneling;

[0018] A real-time monitoring module, which uses sensors to detect the motion state of electrons and the physical characteristics of storage cells during the writing process, for providing real-time feedback;

[0019] A data verification module, which adopts self-verification based on quantum tunneling, for ensuring the integrity and accuracy of the data;

[0020] A low-temperature cooling module, which maintains the temperature of the storage cells within the optimal range through active cooling and passive cooling technologies, for improving the stability of quantum tunneling;

[0021] A wear leveling module, which performs wear leveling on the storage cells, for extending the life of the storage device;

[0022] A multi-layer data verification mechanism module, which performs multi-layer verification at the physical layer, logical layer, and application layer, for improving the reliability of data writing;

[0023] A communication interface module, which supports multiple data transmission protocols, for implementing communication with external devices;

[0024] A power management module, which provides power supply and energy optimization for the writing system.

[0025] The beneficial effects of the present invention are:

[0026] The high-speed writing method and system of the solid-state storage device of the present invention solve the problem that it is difficult to balance low power consumption and high stability in the high-speed writing process of the existing solid-state storage device. Specifically, the parallel quantum tunneling technology is adopted. By applying a specific high-frequency and low-amplitude pulsed voltage across the storage cell, electrons can efficiently and with low power consumption perform quantum tunneling through the insulating layer, thereby realizing high-speed data writing. The adaptive quantum tunneling algorithm dynamically adjusts the writing parameters according to the current state and environmental conditions of the storage device to ensure that each storage cell is in the optimal writing state, improving the writing speed and stability. Further, through a real-time monitoring and feedback mechanism, the system can detect the movement state of electrons and the physical characteristics of the storage cells during the writing process, and adjust the writing parameters in real time according to the feedback results, effectively reducing the writing delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of the high-speed writing method of the solid-state storage device in an embodiment of the present invention.

[0028] Figure 2 It is a schematic diagram of the quantum tunneling principle in an embodiment of the present invention.

[0029] Figure 3 It is a waveform diagram of the pulsed voltage under the condition of quantum tunneling in an embodiment of the present invention.

[0030] Figure 4 It is a schematic diagram of the optimization of the parallel quantum tunneling writing sequence in an embodiment of the present invention.

[0031] Figure 5 It is a diagram of the temperature and cooling power changes during the writing process in an embodiment of the present invention.

[0032] Figure 6 It is a comparison diagram before and after wear leveling processing in an embodiment of the present invention.

[0033] Figure 7 It is a schematic diagram of the multi-layer data verification mechanism in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] The present invention provides the following preferred embodiments:

[0035] Embodiment 1

[0036] In order to solve the problems of slow writing speed and high power consumption of the existing solid-state storage device, this embodiment proposes a high-speed writing method for the solid-state storage device, and its flowchart is as Figure 1 shown. The writing method enables electrons to effectively perform quantum tunneling through the insulating layer by precisely controlling the pulsed voltage parameters, thereby realizing low-power and high-speed data writing, as Figure 2 shown.

[0037] Reference Figure 1 , the steps of the high-speed writing method for a solid-state storage device include:

[0038] S100. Apply a specific pulse voltage across the storage cells of the solid-state storage device, enabling electrons to undergo quantum tunneling through the insulating layer to generate quantum tunneling conditions for low-power data writing.

[0039] S200. Preprocess the storage cells. The preprocessing steps include initialization, state detection, and insulating layer repair to ensure that each storage cell is in a suitable state for writing.

[0040] S300. Determine the optimal writing parameters through an adaptive quantum tunneling algorithm. The adaptive quantum tunneling algorithm dynamically adjusts the voltage and current according to the current state and environmental conditions of the solid-state storage device to optimize the writing speed.

[0041] S400. Simultaneously write multiple storage cells using parallel quantum tunneling, with each storage cell independently controlled to reduce the writing latency.

[0042] S500. Monitor and provide feedback on the writing process in real time. Detect the motion state of electrons and the physical characteristics of the storage cells during the writing process through built-in sensors, and adjust the writing parameters according to the feedback results.

[0043] S600. Use a verification algorithm to verify the data written to the solid-state storage device to ensure data integrity.

[0044] Specifically, apply a specific pulse voltage across the storage cells of the solid-state storage device. The frequency range of the pulse voltage is set to 100 MHz to 200 MHz, and the amplitude range is set to 0.5 V to 1.5 V. The selection of these parameters is based on the physical characteristics of the quantum tunneling effect and the material characteristics of the storage cells. It should be understood that the frequency and amplitude of the pulse voltage affect the quantum tunneling effect. Excessive or insufficient frequency and amplitude will affect the tunneling efficiency of electrons, thereby affecting the writing speed and power consumption. By precisely controlling these parameters, data writing can be ensured under optimal conditions.

[0045] Furthermore, preprocess the storage cells. The preprocessing includes initialization, state detection, and insulation layer repair. The initialization step ensures that the initial states of each storage cell are consistent, facilitating subsequent write operations. The state detection step uses built-in sensors to detect the current state of the storage cells, such as physical parameters like temperature, voltage, and current, to evaluate whether they are suitable for writing. If a fault or insulation layer damage is detected in a storage cell, the insulation layer repair step will be executed. The insulation layer repair can be carried out through heat treatment or chemical treatment, etc., to restore the insulation performance of the storage cells. It can be understood that the preprocessing step can improve the stability of the writing process and reduce the write failure rate caused by poor storage cell states.

[0046] Furthermore, determine the optimal write parameters through the adaptive quantum tunneling algorithm. This algorithm dynamically adjusts the voltage and current according to the current state and environmental conditions of the solid-state storage device to optimize the write speed. Specifically, the adaptive quantum tunneling algorithm first collects the current state data of the solid-state storage device, such as environmental parameters like temperature, humidity, voltage, and current. Then, the algorithm analyzes this data and dynamically adjusts the frequency and amplitude of the pulsed voltage to adapt to the current write conditions. It should be understood that the adaptive quantum tunneling algorithm not only considers the internal state of the storage device but also the influence of the external environment, thus ensuring efficient data writing under various conditions.

[0047] Furthermore, use parallel quantum tunneling to write multiple storage cells simultaneously. Each storage cell is independently controlled to reduce the write latency. Through parallel writing, more data can be processed within the same write time, significantly improving the write speed. Specifically, the controller inside the solid-state storage device is responsible for allocating and managing the write operations of each storage cell. The controller of each storage cell independently applies the pulsed voltage according to the optimal parameters determined by the adaptive quantum tunneling algorithm to achieve data writing. It can be understood that parallel quantum tunneling not only improves the write speed but also reduces the write failures caused by single-point faults.

[0048] Furthermore, monitor and provide feedback on the writing process in real time. Use built-in sensors to detect the motion state of electrons and the physical characteristics of the storage cells during the writing process, such as temperature, voltage, and current. The real-time monitoring data is transmitted to the adaptive quantum tunneling algorithm through the controller, and the algorithm dynamically adjusts the write parameters based on this data to ensure the stability and efficiency of the writing process. It should be understood that the real-time monitoring and feedback mechanism can promptly detect and correct anomalies during the writing process, thereby reducing the risk of write failures and improving the reliability of data writing.

[0049] Further, a verification algorithm is used to verify the data written to the solid-state storage device to ensure the integrity of the data. After the write operation is completed, the verification algorithm verifies the written data to ensure that the data is not lost or damaged. Specifically, the verification algorithm can adopt common data verification methods such as CRC (Cyclic Redundancy Check) or ECC (Error Correction Code). If a data error is detected, the verification algorithm will trigger a data rewrite mechanism to ensure the integrity of the data write. It can be understood that the use of the verification algorithm can further improve the reliability of data writing and ensure that no data loss or damage occurs during the writing process.

[0050] Through this embodiment, high-speed and low-power data writing of the solid-state storage device can be achieved. Specifically, by precisely controlling the pulse voltage parameters, preprocessing steps, adaptive quantum tunneling algorithm, parallel quantum tunneling, real-time monitoring and feedback mechanism, and data verification algorithm, the entire writing process is optimized. This embodiment not only improves the writing speed but also reduces the power consumption and improves the reliability of data writing. High-efficiency and reliable data writing can be achieved under a wide range of environmental conditions, meeting the requirements of high-performance solid-state storage devices.

[0051] Embodiment 2

[0052] To solve the problems of excessive heat generation and low quantum tunneling efficiency when generating quantum tunneling conditions in the solid-state storage device, this embodiment further optimizes the steps of realizing quantum tunneling through high-frequency and low-amplitude pulse voltages. By precisely controlling the pulse voltage parameters, heat generation during the writing process is reduced, and at the same time, the quantum tunneling efficiency is improved, thereby realizing low-power and efficient writing operations.

[0053] First, a high-frequency and low-amplitude pulse voltage is applied across the storage cell of the solid-state storage device. Specifically, the frequency range of the pulse voltage is set to 100 MHz to 200 MHz, and the amplitude range is set to 0.5 V to 1.5 V. The selection of these parameters is based on the physical characteristics of the quantum tunneling effect and the material characteristics of the storage cell. High-frequency pulses can reduce the heat generated by electrons during tunneling, and low-amplitude pulses can avoid damage to the storage cell caused by excessive voltage while ensuring the tunneling efficiency. It should be understood that the combination of high-frequency and low-amplitude pulse voltages can effectively balance heat generation and tunneling efficiency, thereby achieving fast data writing under low-power conditions.

[0054] Further, the pulse parameters are dynamically adjusted through an adaptive pulse adjustment algorithm to ensure the rapid and stable generation of quantum tunneling conditions. The specific expression of the adaptive pulse adjustment algorithm is:

[0055] , where V(t) is the pulse voltage, V 0 is the reference voltage, an and b n are amplitude parameters, f n and g n are frequency parameters, ϕ n and δ n are phase parameters. These parameters are adjusted in real time according to the material properties of the storage unit and the ambient temperature. It can be understood that the adaptive pulse adjustment algorithm ensures the generation of stable quantum tunneling conditions under different environmental conditions by dynamically adjusting the pulse parameters, thereby improving the writing speed.

[0056] Furthermore, when generating quantum tunneling conditions, the adaptive pulse adjustment algorithm makes real-time adjustments according to the physical properties of the storage unit. Specifically, built-in sensors detect environmental parameters such as the temperature, humidity, and voltage of the storage unit in real time and transmit this data to the adaptive pulse adjustment algorithm. Based on this data, the algorithm adjusts the amplitude parameters a n and b n , frequency parameters f n and g n , phase parameters ϕ n and δ n to ensure that the waveform and intensity of the pulse voltage are always in the optimal state, as Figure 3 shown. It should be understood that this real-time adjustment mechanism can promptly respond to environmental changes, maintain the optimal tunneling conditions, and reduce the writing failure rate caused by environmental changes.

[0057] Furthermore, in practical applications, the adaptive pulse adjustment algorithm can be implemented in various ways. For example, the algorithm can be embedded in the controller of the storage device, and the digital signal processor (DSP) is used to perform real-time calculations and adjustments on the pulse voltage parameters. The controller communicates with the sensors of the storage unit to obtain real-time data and adjusts the pulse voltage generation circuit according to the optimal parameters output by the algorithm. It can be understood that this embedded implementation method can improve the execution efficiency of the algorithm, reduce data transmission delays, and ensure the timeliness and accuracy of the writing operation.

[0058] Through this embodiment, a high-frequency and low-amplitude pulse voltage can be applied in the solid-state storage device to generate efficient quantum tunneling conditions. Specifically, by using the adaptive pulse adjustment algorithm to adjust the pulse voltage parameters in real time according to the physical properties of the storage unit, fast and stable data writing can be achieved while maintaining low power consumption. The heat generation during the writing process is reduced, and the quantum tunneling efficiency is improved.

[0059] Embodiment 3

[0060] To solve the problem of write failure caused by charge accumulation and insulation layer damage during the preprocessing of solid-state storage devices, this embodiment further optimizes the steps for preprocessing storage cells. Through charge balancing, real-time detection, and charge trap repair, it is ensured that each storage cell is in a suitable state for writing, thereby improving the stability and reliability of the writing process.

[0061] In this embodiment, charge balancing is first performed on the storage cells. Specifically, by applying a reverse voltage, the charge accumulation in the storage cells is eliminated to ensure that each storage cell is in the same initial state. It can be understood that charge accumulation will cause an increase in the potential difference between storage cells, thereby affecting the tunneling efficiency of electrons and the accuracy of data writing. The application time of the reverse voltage can be set to 100 μs to 200 μs, and the voltage range is -0.5 V to -1.5 V to ensure thorough and efficient charge balancing.

[0062] Furthermore, a detector is used to perform real-time detection on the state parameters of the storage cells. The state parameters include the resistance value, capacitance value, and temperature value of the storage cells. The detector can adopt a high-precision resistance measuring instrument, capacitance measuring instrument, and temperature sensor to detect the resistance value, capacitance value, and temperature value of the storage cells respectively. These data will be transmitted to the controller in real time for evaluating the current state of the storage cells. It should be understood that real-time detection can promptly detect faults or abnormalities in the storage cells, providing accurate data support for subsequent adjustments and repairs, thereby improving the reliability of the writing process.

[0063] Furthermore, charge trap repair is performed on the insulation layer of the storage cells. Specifically, a laser beam is used to scan the insulation layer to eliminate trapped charges to improve the tunneling efficiency. The scanning speed of the laser beam can be adjusted according to the material characteristics of the insulation layer, usually set to 1 μm / s to 5 μm / s. It should be understood that trapped charges will reduce the tunneling efficiency of the insulation layer, increasing the power consumption and failure rate during the writing process. Through laser beam scanning, these trapped charges can be effectively eliminated to restore the performance of the insulation layer.

[0064] Furthermore, during the charge trap repair process, a feedback control algorithm is used to adjust the laser parameters according to the laser beam scanning results. Specifically, the expression of the feedback control algorithm is:

[0065] , where, I laser (t) is the laser current, P target is the target power, P actual (t) is the actual power, R unit (t) is the resistance value of the storage cell, T unit (t) is the temperature value of the storage cell, k 1 and k2 It is a coefficient adjusted according to the characteristics of the insulating layer material. It should be understood that the feedback control algorithm ensures that the power and scanning effect of the laser beam are always in the best state by adjusting the laser current in real time, thereby improving the efficiency and accuracy of insulating layer repair.

[0066] Through this embodiment, charge balance, real-time detection, and charge trap repair during the preprocessing of the solid-state storage device can be achieved. Specifically, charge accumulation is eliminated through a reverse voltage, the state parameters of the storage unit are detected in real time by a high-precision detector, the insulating layer trap charges are repaired by laser beam scanning, and the feedback control algorithm dynamically adjusts the laser parameters to ensure that each storage unit is in a state suitable for writing. The benefits of this embodiment are that it improves the efficiency and reliability of the preprocessing step, reduces the failure rate during the writing process, and enhances the stability and accuracy of data writing.

[0067] Embodiment Four

[0068] To solve the problems of inaccurate parameter adjustment and insufficient adaptability in the adaptive quantum tunneling algorithm of the solid-state storage device, this embodiment further optimizes the adaptive quantum tunneling algorithm. By evaluating the state of the storage unit before writing and updating the machine learning model in real time during the writing process, it ensures that the writing parameters are always in the optimal state, thereby achieving efficient data writing.

[0069] First, before writing, the current state of the solid-state storage device is evaluated by inputting the resistance value, capacitance value, temperature value of the storage unit, and the data characteristics of the previous write into the machine learning model to obtain an evaluation result. Specifically, the machine learning model can adopt a deep neural network (DNN) or other suitable models. The input data characteristics include the current resistance value, capacitance value, temperature value of the storage unit, and the data characteristics of the previous write, such as the number of writes and the type of written data. It can be understood that these input data can comprehensively reflect the current state of the storage unit and provide an accurate evaluation basis for the adaptive quantum tunneling algorithm.

[0070] Furthermore, according to the evaluation result, the writing parameters are dynamically adjusted. The specific steps include: calculating the feature vector x of the current state; using the pre-trained neural network model f for prediction and outputting the optimal writing parameter y. The calculation expression for the optimal writing parameter y is:

[0071] , where A is the feature weight matrix, B is the bias vector, C is the correction weight matrix, and D is the correction bias vector. These parameters are obtained through the pre-trained neural network model and can accurately reflect the current state of the storage unit. It should be understood that through the combination of the feature vector x and the neural network model f, the accurate prediction of the optimal writing parameter can be achieved, thereby improving the efficiency and reliability of the writing process.

[0072] Further, during the writing process, the machine learning model is updated in real time to adapt to the dynamic changes of the storage device. Specifically, the controller collects the writing status data of the storage cells in real time, such as writing voltage, writing current, writing time, etc., and uses this data to update the machine learning model. The update of the machine learning model can embed an online learning module in the controller, and continuously optimize the parameters of the model through incremental learning. It can be understood that this real-time update mechanism can ensure that the machine learning model always adapts to the current state of the storage device, improving the accuracy and adaptability of the adaptive quantum tunneling algorithm.

[0073] Through this embodiment, efficient and low-power data writing of the solid-state storage device can be achieved. Specifically, the current state of the storage cells is evaluated by the machine learning model, and the writing parameters are dynamically adjusted. The machine learning model is updated in real time during the writing process to ensure that the optimal writing parameters can be achieved under various conditions. The accuracy and adaptability of the adaptive quantum tunneling algorithm are improved, the failure rate during the writing process is reduced, and the writing speed and reliability of the solid-state storage device are enhanced.

[0074] Embodiment Five

[0075] To solve the problems of low efficiency and writing delay in the parallel quantum tunneling writing process of the solid-state storage device, this embodiment further optimizes the steps of simultaneously writing multiple storage cells using parallel quantum tunneling. By using a multi-core control circuit, time division multiplexing technology, and dynamic optimal writing order calculation, it is ensured that each storage cell can achieve efficient data writing under independent control and synchronous writing conditions, as Figure 4 shown.

[0076] First, a multi-core control circuit is used to write multiple storage cells simultaneously. The design of the multi-core control circuit adopts a high parallel processing architecture, and each core independently is responsible for the writing operation of a storage cell, thus realizing a synchronous and independent writing process.

[0077] Further, time division multiplexing technology is adopted to decompose the writing operation into multiple time slices, and a group of storage cells are written within each time slice. The time division multiplexing technology realizes efficient management of data writing by allocating the writing operation to different time slices. Specifically, the controller dynamically adjusts the number and order of the storage cells written within each time slice according to the number of storage cells and the writing ability of each time slice. It can be understood that the time division multiplexing technology can effectively balance the writing time of each storage cell, reduce the global writing delay, and improve the overall writing efficiency.

[0078] Further, the optimal writing order of each time slice is calculated using the following expression:

[0079] , where s represents the optimal write order, which is an index vector of storage units and represents the order of write units within each time slice, s' represents the candidate write order, which represents a possible write order, and argmax s' represents selecting the candidate write order s' that maximizes the value of f(s'), N represents the total number of storage units, n represents the nth storage unit, and d s'(n) represents the distance from the nth storage unit to the control circuit, and R s'(n) represents the resistance value of the nth storage unit, and C s'(n) represents the capacitance value of the nth storage unit, and log(1 + C s'(n) ) represents taking the logarithm of the capacitance value and adding 1 to avoid a denominator of 0 and balance the impact of the capacitance value. It can be understood that this calculation method of the dynamic optimal write order can achieve an efficient data write order according to the specific physical characteristics of the storage units, reducing conflicts and delays during the write process.

[0080] Furthermore, to ensure the accuracy and efficiency of the calculation, the controller can use a high-performance mathematical operation unit (FPU) and an optimized algorithm library. Specifically, the FPU can accelerate floating-point operations and improve the calculation speed; the optimized algorithm library can provide efficient implementations of mathematical functions and reduce the consumption of computing resources. It can be understood that through the combined optimization of hardware and software, the efficiency of calculating the optimal write order can be significantly improved, ensuring the real-time performance and accuracy of the write process.

[0081] Furthermore, the controller is also equipped with high-speed data transmission interfaces, such as PCIe 4.0 and DDR4 or DDR5 memory interfaces, to support high-speed data read and write and transmission. These interfaces can significantly increase the data transmission rate, reduce data transmission latency, and ensure the efficient progress of write operations. It should be understood that the high-speed data transmission interface is an important support for achieving efficient parallel writing, which can ensure the rapid transfer and synchronous writing of data between various storage units.

[0082] Through this embodiment, the write efficiency of the solid-state storage device can be improved. Specifically, the multi-core control circuit realizes parallel processing and independent control, the time-division multiplexing technology balances the write time of each storage unit, and the dynamic optimal write order calculation optimizes the write order according to the physical characteristics of the storage units. The comprehensive application of these technologies ensures that efficient data writing can be achieved under various conditions. It improves the parallelism and real-time performance of the write operation, reduces the write latency, and enhances the write performance of the solid-state storage device.

[0083] Embodiment Six

[0084] To address the issues of insufficient accuracy and low verification efficiency in the data verification process of solid-state storage devices, this embodiment further optimizes the steps of using a verification algorithm to verify the written data. Data verification is performed through the electron motion state during the quantum tunneling process, and a multi-level verification algorithm is adopted to verify the low-level and high-level features of the data respectively, thereby ensuring the integrity and accuracy of the data.

[0085] First, data verification is performed through the electron motion state during the quantum tunneling process. Specifically, the built-in sensor will detect the motion state of electrons during tunneling in real time, including the energy level change, tunneling time, tunneling current, etc. of the electrons. These data will be transmitted to the verification algorithm for evaluating the correctness of data writing. It should be understood that the detection of the electron motion state can provide direct feedback on the writing process, helping to promptly detect anomalies during the writing process and improve the accuracy of verification.

[0086] Furthermore, a multi-level verification algorithm is used to verify the low-level and high-level features of the data respectively. The multi-level verification algorithm includes physical layer verification, logical layer verification, and application layer verification. Physical layer verification mainly focuses on the physical characteristics of storage units, such as voltage, current, and temperature, etc.; logical layer verification mainly focuses on the logical structure of data, such as the error rate of data blocks and check codes, etc.; application layer verification mainly focuses on the application layer characteristics of data, such as data hash values and verification strength, etc. It can be understood that the independent operation and mutual verification of the multi-level verification algorithm can comprehensively cover all aspects of data verification and ensure the integrity and accuracy of the data.

[0087] Furthermore, the specific steps of physical layer verification are as follows: Calculate the low-level feature vector F 1 , where F 1 includes physical parameters such as the voltage, current, and temperature of the storage unit. The following expression is used for physical layer verification:

[0088] , where E is the low-level feature weight matrix, G is the high-level feature weight matrix, H is the temperature weight matrix, and T is the temperature vector. It can be understood that physical layer verification can promptly detect anomalies at the physical level by comprehensively considering the physical characteristics of storage units and improve the accuracy of data verification.

[0089] Furthermore, the specific steps of logical layer verification are as follows: Calculate the high-level feature vector F 2 , where F 2 includes logical parameters such as the data type, data length, and check code of the logical block. The following expression is used for logical layer verification:

[0090] , it can be understood that by comprehensively considering the logical structure of the data, the logical layer verification can promptly detect anomalies at the logical level and improve the reliability of data verification.

[0091] Furthermore, the specific steps of the application layer verification are as follows: Calculate the low-level feature vector F 1 and the high-level feature vector F 2 of each application layer data block, and perform comprehensive verification using the temperature vector T. The specific expression is also:

[0092] , where E, G, and H are the low-level feature weight matrix, high-level feature weight matrix, and temperature weight matrix respectively. These weight matrices can be adjusted according to the actual situation of the storage device to optimize the verification effect. It should be understood that by comprehensively considering the application layer characteristics of the data, the application layer verification can provide a higher level of verification guarantee to ensure the integrity of the data at the application layer.

[0093] Furthermore, to improve the execution efficiency of the verification algorithm, a high-performance mathematical operation unit (FPU) and an optimized algorithm library can be embedded in the controller. Specifically, the FPU can accelerate floating-point operations and improve the verification speed; the optimized algorithm library can provide efficient implementation of mathematical functions and reduce the consumption of computing resources. It can be understood that through the optimized combination of hardware and software, the execution efficiency of the multi-level verification algorithm can be significantly improved, ensuring the timeliness and accuracy of data verification.

[0094] Through this embodiment, the data verification accuracy and efficiency of the solid-state storage device can be improved. Specifically, by using the electron motion state in the quantum tunneling process for data verification and adopting a multi-level verification algorithm to verify the low-level and high-level features of the data respectively, all aspects of data verification can be comprehensively covered to ensure the integrity and accuracy of the data.

[0095] Embodiment Seven

[0096] To solve the problems of reduced data writing efficiency and decreased data reliability caused by excessive temperature during the writing process of the solid-state storage device, this embodiment further optimizes the method of using low-temperature cooling to maintain the temperature range of the storage unit during the writing process. Through the combination of active cooling and passive cooling, and the calculation of dynamic optimal cooling power, it is ensured that the temperature of the storage unit is always within the optimal range during the writing process, thereby improving the writing speed of quantum tunneling and data reliability.

[0097] First, during the writing process, low-temperature cooling is used to maintain the temperature range of the storage unit. Specifically, the low-temperature cooling system includes two parts: active cooling and passive cooling. The active cooling system is controlled by a micro-cooling system, and the passive cooling system is achieved through heat dissipation materials and structural design. It should be understood that the active cooling system can adjust the cooling power in real time during the writing process to ensure temperature stability, as Figure 5 shown; the passive cooling system provides continuous heat dissipation through heat dissipation materials and structural design, reducing temperature fluctuations.

[0098] Furthermore, the active cooling system uses the following expression to calculate the optimal cooling power:

[0099] , where P cool is the cooling power, T current is the current temperature, T target is the target temperature, V write is the writing voltage, I write is the writing current, and k 3 is the adjustment coefficient. It can be understood that the calculation method of the optimal cooling power can dynamically adjust the cooling power by comprehensively considering the current temperature, target temperature, and writing parameters of the storage unit, ensuring that the temperature of the storage unit is always within the optimal range.

[0100] Furthermore, the passive cooling system can be achieved by selecting high-performance heat dissipation materials and optimized structural design. Specifically, heat dissipation materials such as metal alloys with high thermal conductivity or graphene can be used, and the structural design can improve the heat dissipation effect through micro heat sinks or heat dissipation channels. It should be understood that high-performance heat dissipation materials and optimized structural design can significantly improve the heat dissipation effect of the storage unit, reduce temperature fluctuations, and ensure the stability of the writing process.

[0101] Furthermore, the controller will monitor the temperature change of the storage unit in real time and adjust the parameters of the low-temperature cooling system according to the temperature change. Specifically, the controller obtains the temperature data of the storage unit in real time through the built-in temperature sensor and transmits this data to the optimal cooling power calculation algorithm. The algorithm dynamically adjusts the power of the cooling system according to this data to ensure that the temperature is always within the optimal range. It can be understood that the real-time monitoring and adjustment mechanism can respond to temperature changes in a timely manner, reduce the impact of temperature fluctuations on the writing process, and improve the writing efficiency and data reliability.

[0102] Furthermore, to improve the execution efficiency of the cooling system, a high-performance floating-point unit (FPU) and an optimized algorithm library can be embedded in the controller. Specifically, the FPU can accelerate floating-point operations and improve the speed of cooling power calculation; the optimized algorithm library can provide efficient implementation of mathematical functions and reduce the consumption of computing resources. It can be understood that through the combined optimization of hardware and software, the execution efficiency of the cooling system can be significantly improved, ensuring the real-time performance and accuracy of the cooling process.

[0103] Through this embodiment, efficient low-temperature cooling can be achieved during the writing process of the solid-state storage device, keeping the temperature of the storage unit always within the optimal range. Specifically, through the combination of active cooling and passive cooling, and the calculation of dynamic optimal cooling power, the impact of temperature fluctuations on data writing can be significantly reduced, and the writing speed can be improved. Through the comprehensive application of various cooling technologies, the temperature stability during the writing process is ensured, and the writing efficiency of quantum tunneling is improved.

[0104] Embodiment VIII

[0105] To solve the problems of low data writing efficiency and reduced reliability caused by uneven wear of storage units during the writing process of solid-state storage devices, this embodiment further optimizes the steps of wear leveling the storage units of the storage device before writing, as Figure 6 shown. By calculating the wear level of each storage unit and using a wear leveling algorithm to redistribute the written data, it is ensured that the wear level of each unit is uniform, thereby improving the reliability and efficiency of data writing.

[0106] First, before writing, the storage units of the storage device are subjected to wear leveling. Specifically, the wear level W i of each storage unit is calculated to evaluate its current wear state. The wear level W i can be comprehensively evaluated through parameters such as the number of writes, write voltage, and write current. It can be understood that the calculation of the wear level can comprehensively reflect the wear condition of the storage unit and provide an accurate basis for wear leveling.

[0107] Furthermore, a wear leveling algorithm is used to redistribute the written data. Specifically, the wear leveling algorithm aims to make the wear level of each storage unit as uniform as possible by redistributing the data. The expression of the algorithm is:

[0108] , where D new is the data matrix after redistribution, D oldis the original data matrix, W is the wear level matrix, and Q and U are adjustment matrices. It should be understood that by introducing the wear level matrix W and the adjustment matrices Q and U, this expression can dynamically adjust the data allocation strategy, giving priority to writing data to storage units with lower wear levels, thereby achieving wear leveling.

[0109] Furthermore, the specific implementation of the wear leveling algorithm can be carried out in the controller. The controller can use a high-performance embedded processor, such as the NXP i.MX 8QuadXPlus, equipped with 4 GB of LPDDR4 memory and 32 GB of eMMC storage, to support efficient wear leveling processing. It should be understood that a high-performance embedded processor can handle complex wear leveling algorithms to ensure the efficiency and accuracy of data allocation.

[0110] Furthermore, the wear leveling algorithm also takes into account the physical characteristics and current working status of the storage units. Specifically, the controller will monitor parameters such as the temperature, voltage, and current of each storage unit in real time and incorporate this data into the wear leveling calculation. For example, a storage unit with a higher temperature may be cooled first rather than immediately written to, to reduce the impact of high temperature on the writing process. It can be understood that by comprehensively considering the physical characteristics and current working status, the wear leveling algorithm can more comprehensively optimize the data writing strategy, ensuring a more stable and reliable writing process for each storage unit.

[0111] Furthermore, the wear leveling process can also be combined with a multi-core control circuit and time-division multiplexing technology to further optimize the writing process. Specifically, the multi-core control circuit can parallelly process the wear level calculation and data allocation of multiple storage units, and the time-division multiplexing technology can decompose the data allocation and writing process into multiple time slices, with a part of the wear leveling and data writing completed within each time slice. It should be understood that this combination can give full play to the parallel advantages of multi-core processing and the time management advantages of time-division multiplexing, further improving the writing efficiency and data reliability.

[0112] Through this embodiment, efficient wear leveling processing can be achieved during the writing process of the solid-state storage device. Specifically, by calculating the wear level of each storage unit and using the wear leveling algorithm to reallocate the written data, it is ensured that the wear level of each storage unit is uniform, thereby improving the reliability and efficiency of data writing. The benefit of this embodiment is that through wear leveling processing, the service life of the storage device can be extended, the performance degradation and data reliability reduction caused by uneven wear can be reduced, and the overall writing performance and storage stability can be improved.

[0113] Embodiment Nine

[0114] To address the issues of insufficient data reliability and high write error rate caused by a single verification mechanism during data writing in solid-state storage devices, this embodiment further optimizes the steps of adopting a multi-layer data verification mechanism. Through the comprehensive application of physical layer verification, logical layer verification, and application layer verification, multi-faceted verification of data is ensured, improving the reliability of writing.

[0115] First, adopt a multi-layer data verification mechanism to improve the reliability of writing through multi-faceted verification of data. The multi-layer data verification mechanism includes physical layer verification, logical layer verification, and application layer verification. Each layer's verification algorithm runs independently and verifies each other, as Figure 7 shown, thus ensuring the integrity and accuracy of data at different levels.

[0116] Furthermore, the physical layer verification calculates the verification value using the following expression:

[0117] , where C phy is the physical layer verification value, V n is the voltage vector of the nth storage unit, I n is the current vector of the nth storage unit, T n is the temperature vector of the nth storage unit, and N is the total number of storage units. It can be understood that by comprehensively considering physical parameters such as the voltage, current, and temperature of the storage unit, the physical layer verification can promptly detect abnormalities at the physical level and improve the accuracy of data verification.

[0118] Furthermore, the logical layer verification calculates the verification value using the following expression:

[0119] , where C log is the logical layer verification value, D i is the data vector of the ith logical block, W i is the weight vector of the ith logical block, R log,i is the error rate of the ith logical block, and M is the total number of logical blocks. It can be understood that by comprehensively considering the logical structure and error rate of the data, the logical layer verification can promptly detect abnormalities at the logical level and improve the reliability of data verification.

[0120] Furthermore, the application layer verification calculates the verification value using the following expression:

[0121] , where C app is the application layer verification value, H j is the data hash vector of the jth application layer, S j is the verification code vector of the jth application layer, Q app,jis the verification strength of the j-th application layer, and K is the total number of application layer data blocks. It can be understood that application layer verification can provide a higher level of verification guarantee by comprehensively considering the application layer characteristics of data, such as data hash values and verification strength, to ensure the integrity of data at the application layer.

[0122] Furthermore, the independent operation and mutual verification of the multi-layer verification mechanism can ensure the comprehensiveness and reliability of data verification. Specifically, physical layer verification, logical layer verification, and application layer verification can operate independently within different time slices, and each verification result will be transmitted to the controller for verification and integration. It should be understood that this mechanism of independent operation and mutual verification can comprehensively cover all aspects of data verification to ensure the integrity and accuracy of data at different levels.

[0123] Furthermore, the multi-layer verification mechanism can also be combined with a low-temperature cooling system and wear leveling processing to further optimize the writing process. Specifically, the low-temperature cooling system can keep the temperature of the storage unit stable during the writing process, reducing the impact of high temperature on the writing process; wear leveling processing can ensure that the wear degree of each storage unit is uniform, extending the service life of the storage device. It can be understood that by combining the multi-layer verification mechanism with low-temperature cooling and wear leveling processing, the reliability of data writing can be significantly improved, the writing error rate can be reduced, and the overall performance of the solid-state storage device can be enhanced.

[0124] The benefits of this embodiment are that through the comprehensive application of the multi-layer data verification mechanism, all aspects of data verification can be comprehensively covered to ensure the integrity and accuracy of data at the physical layer, logical layer, and application layer. Specifically, physical layer verification focuses on the physical characteristics of the storage unit, logical layer verification focuses on the logical structure of the data, and application layer verification focuses on the application layer characteristics of the data. This multi-level verification mechanism can improve the accuracy of verification, reduce data errors during the writing process, and enhance the writing performance of the solid-state storage device.

[0125] Embodiment Ten

[0126] To solve various problems that may be encountered during the high-speed writing process of solid-state storage devices, such as high-precision voltage control, storage unit preprocessing, adaptive writing parameter adjustment, parallel writing, real-time monitoring, data verification, low-temperature cooling, and wear leveling, etc., this embodiment further optimizes each module of the high-speed writing system. Through the comprehensive design of the system, the efficiency, accuracy, and reliability of data writing are ensured.

[0127] First, the writing system includes a high-precision voltage generation module, which is implemented by using a high-precision voltage controller and is used to generate pulsed voltages under quantum tunneling conditions. Specifically, the voltage generation module can use the AD5791 high-precision DAC (Digital-to-Analog Converter) of Analog Devices, Inc., which has an accuracy of up to 16 bits and an output voltage range of -10 V to +10 V. It should be understood that high-precision voltage control can ensure the accuracy and stability of voltage pulses during the quantum tunneling process, thereby improving the accuracy and reliability of the writing operation.

[0128] Furthermore, the design of the preprocessing module includes functions such as charge balancing, state detection, and insulating layer repair for preprocessing the storage cells. The charge balancing function is achieved by using a charge pump circuit to ensure uniform charge distribution in the storage cells before writing. The state detection function uses built-in sensors to monitor the current state of the storage cells in real time, such as voltage, current, and temperature, etc., in order to adjust the preprocessing parameters in a timely manner. The insulating layer repair function repairs the insulating layer of the storage cells by using nanoscale repair materials and technologies to ensure its stable performance. It can be understood that the comprehensive functions of the preprocessing module can significantly improve the readiness of the storage cells and ensure the smooth progress of the writing operation.

[0129] Furthermore, the adaptive quantum tunneling algorithm module is used to dynamically adjust the writing parameters according to the current state of the storage device and environmental conditions. Specifically, the adaptive algorithm module can collect data such as current, voltage, and temperature of the storage device in real time through a controller, and dynamically adjust the writing voltage and writing time based on these data. The algorithm module can use the ARM Cortex-A72 processor in the embedded system, with a processing speed of up to 1.5 GHz and equipped with 4 GB of DDR4 memory to support efficient adaptive algorithm processing. It should be understood that the dynamic adjustment ability of the adaptive algorithm module can ensure that the parameters of the writing operation are always in the optimal state under different environmental conditions, improving the writing efficiency and data reliability.

[0130] Furthermore, the parallel writing module simultaneously writes multiple storage cells through a multi-core control circuit to achieve parallel quantum tunneling and support high-parallel processing. Each core independently is responsible for the writing operation of one storage cell, and significantly improves the writing speed through parallel processing. It can be understood that the high-parallel processing ability of the parallel writing module can significantly reduce the writing time and ensure stable performance when writing a large amount of data.

[0131] Furthermore, the real-time monitoring module uses sensors to detect the electronic motion state and physical characteristics of the storage unit during the writing process and provides real-time feedback. Specifically, the real-time monitoring module can use highly sensitive Hall effect sensors and thermocouple sensors to monitor the electronic motion state and temperature changes respectively. The sensor data is transmitted to the controller through a high-speed interface, and the controller adjusts the writing parameters in real time according to this data. It should be understood that the high-precision sensors of the real-time monitoring module can promptly detect abnormal situations during the writing process and ensure the accuracy and stability of the writing operation.

[0132] Furthermore, the data verification module adopts self-verification based on quantum tunneling to ensure the integrity and accuracy of the data. Specifically, the data verification module can perform real-time verification through the electronic motion state during the quantum tunneling process and combine multi-level verification algorithms to verify the low-level and high-level features of the data. For example, the low-level features include physical parameters such as voltage, current, and temperature, and the high-level features include logical parameters such as data type, data length, and check code. It can be understood that the self-verification based on quantum tunneling can provide real-time feedback and improve the accuracy and reliability of verification.

[0133] Furthermore, the low-temperature cooling module maintains the temperature of the storage unit within the optimal range through active cooling and passive cooling technologies, improving the stability of quantum tunneling. Specifically, the active cooling system can use a micro Peltier cooler, and the controller adjusts the cooling power in real time to keep the temperature within the target range. The passive cooling system can use high-performance graphene heat dissipation materials and optimized heat dissipation structure designs, such as microchannel heat sinks, to provide continuous heat dissipation effects. It should be understood that the dual cooling technology of the low-temperature cooling module can ensure the temperature stability of the storage unit during the writing process and reduce the impact of high temperature on the writing performance.

[0134] Furthermore, the wear leveling module performs wear leveling processing on the storage units to ensure that the wear degree of each unit is uniform and extends the life of the storage device. Specifically, the wear leveling module can calculate the wear degree of each storage unit through the controller and use the wear leveling algorithm to redistribute the written data.

[0135] Furthermore, the multi-layer data verification mechanism module improves the reliability of data writing through multi-layer verification at the physical layer, logical layer, and application layer. Specifically, the physical layer verification calculates the physical layer verification value of each storage unit, the logical layer verification calculates the logical layer verification value of each logical block, and the application layer verification calculates the verification value of each application layer data block. It should be understood that the independent operation and mutual verification of the multi-layer data verification mechanism can comprehensively cover all aspects of data verification and ensure the integrity and accuracy of data at different levels.

[0136] Furthermore, the communication interface module supports multiple data transmission protocols to achieve efficient communication with external devices. Specifically, the communication interface module can support multiple high-speed data transmission protocols such as PCIe 4.0, DDR4, SATA 3.0, and USB 3.2, ensuring high-speed and low-latency data transmission. It should be understood that the support for multiple high-speed data transmission protocols can improve the flexibility and reliability of data transmission, ensuring the efficient operation of the writing system.

[0137] Furthermore, the power management module provides power supply and energy optimization for the writing system. Specifically, the power management module should support a wide input voltage range and high-efficiency output. In addition, the power management module can also dynamically adjust the power consumption of the system through intelligent power management algorithms to ensure the stability and efficiency of the system under high-load conditions. It can be understood that efficient power management and dynamic power consumption adjustment can reduce system energy consumption and improve the overall performance and reliability of the system.

[0138] Through this embodiment, various optimizations such as high-precision voltage control, comprehensive preprocessing, adaptive writing parameter adjustment, parallel writing, real-time monitoring, multi-level data verification, low-temperature cooling, and wear leveling can be achieved during the high-speed writing process of the solid-state storage device. Specifically, the high-precision voltage generation module ensures the precise stability of voltage pulses during the quantum tunneling process; the preprocessing module improves the readiness of storage cells through charge balance, state detection, and insulating layer repair; the adaptive quantum tunneling algorithm module dynamically adjusts writing parameters according to the current state and environmental conditions to ensure the efficiency of writing operations; the parallel writing module achieves parallel processing through a multi-core control circuit to improve the writing speed; the real-time monitoring module provides real-time feedback using high-precision sensors to ensure the accuracy and stability of writing operations; the data verification module adopts self-verification based on quantum tunneling and multi-level verification algorithms to improve the reliability and accuracy of data verification; the low-temperature cooling module maintains temperature stability through active and passive cooling technologies to improve the stability of quantum tunneling; the wear leveling module reduces uneven wear of storage cells through a dynamic data allocation strategy to extend the service life of the device; the communication interface module supports multiple high-speed data transmission protocols to improve the flexibility of data transmission; the power management module reduces system energy consumption and improves overall performance through efficient power management and dynamic power consumption adjustment.

[0139] The benefits of this embodiment are that through the comprehensive design of the system, the efficiency, accuracy, and reliability of the solid-state storage device during high-speed writing are ensured. Specifically, the various modules of the system cooperate with each other to form a complete and optimized data writing process, which can improve the speed and accuracy of data writing, reduce the error rate, extend the service life of the device, and enhance the overall performance and stability. Through this embodiment, the high-speed writing system of the solid-state storage device can operate stably under various environmental conditions and meet the requirements of high data throughput and high speed.

[0140] The above embodiments have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-speed writing method for a solid-state storage device, characterized in that: The writing method comprises the following steps: Applying a specific pulse voltage across the storage unit of the solid-state storage device to enable electrons to quantum tunnel through the insulating layer, thereby generating quantum tunneling conditions for writing data with low power consumption; Preprocessing the storage unit, wherein the preprocessing steps include initialization, state detection and insulation layer repair, to ensure that each storage unit is in a state suitable for writing; determining optimal write parameters by an adaptive quantum tunneling algorithm that dynamically adjusts voltage and current according to a current state and environmental conditions of the solid-state storage device to optimize write speed; Using parallel quantum tunneling to write to multiple storage cells simultaneously, each storage cell is independently controlled to reduce write delay; Real-time monitoring and feedback of the writing process are performed, the motion state of the electrons and the physical properties of the storage unit during the writing process are detected by built-in sensors, and the writing parameters are adjusted according to the feedback results; Verifying the data written into the solid-state storage device using a verification algorithm to ensure the integrity of the data; The step of generating quantum tunneling conditions comprises: Applying a high-frequency, low-amplitude pulse voltage across the storage unit to reduce heat generation during writing and improve quantum tunneling efficiency; Adjusting pulse parameters, wherein the pulse parameters include a frequency range and an amplitude range, wherein the frequency range is adjusted to 100 MHz to 200 MHz, and the amplitude range is adjusted to 0.5 V to 1.5 V, and by controlling the pulse parameters, quantum tunneling conditions are generated quickly and stably; When generating the quantum tunneling condition, an adaptive pulse adjustment algorithm is used. The adaptive pulse adjustment algorithm dynamically adjusts the frequency range and the amplitude range according to the physical characteristics of the storage unit. The adaptive pulse adjustment algorithm is expressed as: , where V(t) is the pulse voltage, V0 is the reference voltage, and a n and b n is the amplitude parameter, f n and g n is the frequency parameter, ϕ n and δ n is the phase parameter, n represents the nth storage unit, and N is the total number of storage units; The steps of preprocessing the storage unit include: Performing charge balancing on the storage cells, eliminating charge accumulation in the storage cells by applying a reverse voltage, so as to ensure that each of the storage cells is in the same initial state; Using a detector to perform real-time detection on the state parameters of the storage unit, the state parameters including the resistance value, capacitance value and temperature value of the storage unit; Performing charge trap repair on the insulating layer of the memory cell, scanning the insulating layer with a laser beam to eliminate trapped charges, so as to improve tunneling efficiency; During the charge trap repair process, a feedback control algorithm is used to adjust the laser parameters according to the laser beam scanning results. The expression of the feedback control algorithm is: , where I laser (t) is the laser current, P target is the target power, P actual (t) is the actual power, R unit (t) is the resistance value of the memory cell, T unit (t) is the temperature value of the storage unit, k1 and k2 are coefficients adjusted according to the material properties of the insulation layer; The adaptive quantum tunneling algorithm comprises: Before writing, inputting the resistance value, capacitance value, temperature value and previously written data features of the storage unit through a machine learning model to evaluate the current state of the solid-state storage device and obtain an evaluation result; According to the evaluation result, dynamically adjusting the write parameters, the steps include: Calculate the characteristic vector x of the current state; Use the pre-trained neural network model f to make predictions and output the optimal write parameter y; The optimal write parameters are calculated using the following expression: , where A is the feature weight matrix, B is the bias vector, C is the correction weight matrix, and D is the correction bias vector; During the writing process, the machine learning model is updated in real time to adapt to the dynamic changes of the storage device; The step of simultaneously writing into a plurality of storage cells by parallel quantum tunneling comprises: Using a multi-core control circuit to write to a plurality of said storage units simultaneously, each of said storage units being independently controlled to synchronize and independently write operations; Decomposing the write operation into multiple time slices by using time division multiplexing, and writing a group of the storage units in each time slice to improve the write efficiency; The optimal write order for each of the time slices is calculated using the following expression: , where s represents the optimal write order, is an index vector of a storage unit, represents the order of writing units in each time slice, s' represents the candidate write order, represents the possible write order, and d s'(n) Represents the distance from the nth storage unit to the control circuit, R s'(n) Represents the resistance value of the nth memory cell, C s'(n) represents the capacitance value of the nth storage unit, log(1+C s'(n) ) means taking the logarithm of the capacitance value and adding 1 to avoid the situation where the denominator is 0 and to balance the influence of the capacitance value.

2. The high-speed writing method for a solid-state storage device according to claim 1, characterized in that: The step of using a verification algorithm to verify the data written into the solid-state storage device comprises: Data verification is performed through the electron motion state during quantum tunneling to improve verification accuracy; Use a multi-level verification algorithm to verify the low-level and high-level features of the data to ensure data integrity; The verification algorithm comprises the following steps: Calculate the low-level feature vector F1 of the data; Calculate the high-level eigenvector F2 of the data; Use the following expression for comprehensive verification: , where E is the low-level feature weight matrix, G is the high-level feature weight matrix, H is the temperature weight matrix, and T is the temperature vector.

3. The high-speed writing method for a solid-state storage device according to claim 1, characterized in that: The writing method further comprises the following steps: During the writing process, cryogenic cooling is used to maintain the temperature range of the memory cell to increase the writing speed of quantum tunneling; The low temperature cooling includes active cooling and passive cooling, the active cooling is controlled by a micro cooling system, and the passive cooling is achieved by heat dissipation materials and structural design; The active cooling calculates the optimal cooling power using the following expression: , where P cool is the cooling power, T current is the current temperature, T target is the target temperature, V write is the write voltage, I write is the write current, and k3 is the adjustment coefficient.

4. The high-speed writing method for a solid-state storage device according to claim 1, wherein: The writing method further comprises the following steps: Before writing, the storage unit of the storage device is wear-leveled to ensure that the wear degree of each unit is uniform; The wear leveling processing step comprises: Calculate the wear level W of each storage unit i , and list the wear degree matrix W; Use wear leveling algorithm to redistribute written data to ensure even wear; The expression of the wear leveling algorithm is: , where D new is the redistributed data matrix, D old is the original data matrix, W is the wear degree matrix, Q and U are adjustment matrices.

5. The high-speed writing method for a solid-state storage device according to claim 1, wherein: The writing method further comprises the following steps: Adopt a multi-layer data verification mechanism to improve the reliability of writing by verifying data in many aspects; The multi-layer data verification mechanism includes physical layer verification, logical layer verification and application layer verification, and the verification algorithm of each layer runs independently and verifies each other; The physical layer checksum uses the following expression to calculate the checksum value: , where C phy is the physical layer check value, V n is the voltage vector of the nth storage unit, I n is the current vector of the nth storage unit, T n is the temperature vector of the nth storage unit, N is the total number of storage units; The logic layer checksum uses the following expression to calculate the checksum value: , where C log is the logic layer checksum, D i is the data vector of the i-th logic block, W i is the weight vector of the i-th logic block, R log,i is the error rate of the ith logic block, M is the total number of logic blocks; The application layer checksum uses the following expression to calculate the checksum value: , where C app is the application layer checksum value, H j is the data hash vector of the jth application layer, S j is the check code vector of the jth application layer, Q app,j is the checksum strength of the jth application layer, and K is the total number of application layer data blocks.

6. A high-speed writing system for a solid-state storage device, characterized in that: The writing system comprises: A voltage generation module, implemented by a high-precision voltage controller, is used to generate pulse voltage under quantum tunneling conditions; A pre-processing module, including charge balancing, state detection and insulation layer repair, is used to pre-process the storage unit; An adaptive quantum tunneling algorithm module that dynamically adjusts write parameters based on the current state of the storage device and environmental conditions; A parallel writing module, which writes to multiple storage cells simultaneously through a multi-core control circuit to achieve parallel quantum tunneling; A real-time monitoring module uses sensors to detect the electron motion state and physical characteristics of the storage unit during the writing process to provide real-time feedback; The data verification module uses self-verification based on quantum tunneling to ensure data integrity and accuracy; A cryogenic cooling module that uses active and passive cooling techniques to keep the temperature of the storage unit within an optimal range to improve the stability of quantum tunneling; A wear leveling module performs wear leveling on storage units to extend the life of storage devices; The multi-layer data verification mechanism module is used to improve the reliability of data writing through multi-layer verification at the physical layer, logical layer and application layer; Communication interface module, supporting multiple data transmission protocols, used to achieve communication with external devices; A power management module, providing power supply and energy optimization for the writing system; The step of generating quantum tunneling conditions comprises: Applying a high-frequency, low-amplitude pulse voltage across the storage unit to reduce heat generation during writing and improve quantum tunneling efficiency; Adjusting pulse parameters, wherein the pulse parameters include a frequency range and an amplitude range, wherein the frequency range is adjusted to 100 MHz to 200 MHz, and the amplitude range is adjusted to 0.5 V to 1.5 V, and by controlling the pulse parameters, quantum tunneling conditions are generated quickly and stably; When generating the quantum tunneling condition, an adaptive pulse adjustment algorithm is used. The adaptive pulse adjustment algorithm dynamically adjusts the frequency range and the amplitude range according to the physical characteristics of the storage unit. The adaptive pulse adjustment algorithm is expressed as: , where V(t) is the pulse voltage, V0 is the reference voltage, and a n and b n is the amplitude parameter, f n and g n is the frequency parameter, ϕ n and δ n is the phase parameter, n represents the nth storage unit, and N is the total number of storage units; The steps of preprocessing the storage unit include: Performing charge balancing on the storage cells, eliminating charge accumulation in the storage cells by applying a reverse voltage, so as to ensure that each of the storage cells is in the same initial state; Using a detector to perform real-time detection on the state parameters of the storage unit, the state parameters including the resistance value, capacitance value and temperature value of the storage unit; Performing charge trap repair on the insulating layer of the memory cell, scanning the insulating layer with a laser beam to eliminate trapped charges, so as to improve tunneling efficiency; During the charge trap repair process, a feedback control algorithm is used to adjust the laser parameters according to the laser beam scanning results. The expression of the feedback control algorithm is: , where I laser (t) is the laser current, P target is the target power, P actual (t) is the actual power, R unit (t) is the resistance value of the memory cell, T unit (t) is the temperature value of the storage unit, k1 and k2 are coefficients adjusted according to the material properties of the insulation layer; The adaptive quantum tunneling algorithm comprises: Before writing, inputting the resistance value, capacitance value, temperature value and previously written data features of the storage unit through a machine learning model to evaluate the current state of the solid-state storage device and obtain an evaluation result; According to the evaluation result, dynamically adjusting the write parameters, the steps include: Calculate the characteristic vector x of the current state; Use the pre-trained neural network model f to make predictions and output the optimal write parameter y; The optimal write parameters are calculated using the following expression: , where A is the feature weight matrix, B is the bias vector, C is the correction weight matrix, and D is the correction bias vector; During the writing process, the machine learning model is updated in real time to adapt to the dynamic changes of the storage device; The step of simultaneously writing into a plurality of storage cells by parallel quantum tunneling comprises: Using a multi-core control circuit to write to a plurality of said storage units simultaneously, each of said storage units being independently controlled to synchronize and independently write operations; Decomposing the write operation into multiple time slices by using time division multiplexing, and writing a group of the storage units in each time slice to improve the write efficiency; The optimal write order for each of the time slices is calculated using the following expression: , where s represents the optimal write order, is an index vector of a storage unit, represents the order of writing units in each time slice, s' represents the candidate write order, represents the possible write order, and d s'(n) Represents the distance from the nth storage unit to the control circuit, R s'(n) Represents the resistance value of the nth memory cell, C s'(n) represents the capacitance value of the nth storage unit, log(1+C s'(n) ) means taking the logarithm of the capacitance value and adding 1 to avoid the situation where the denominator is 0 and to balance the influence of the capacitance value.

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