Patent Publication Number: US-10784174-B2

Title: Method and apparatus for determining etch process parameters

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present disclosure is related to U.S. patent application Ser. No. 14/863,211 entitled “APPARATUS FOR DETERMINING PROCESS RATE” by Albarede et al., filed on Sep. 23, 2015 and U.S. Pat. No. 9,735,069 entitled “METHOD AND APPARATUS FOR DETERMINING PROCESS RATE” by Kabouzi et al., filed on Sep. 23, 2015 and issued on Aug. 15, 2017, which are incorporated by reference for all purposes. 
     BACKGROUND 
     The present disclosure relates to the manufacturing of semiconductor devices. More specifically, the disclosure relates to etching used in manufacturing semiconductor devices. 
     During semiconductor wafer processing, silicon containing layers are selectively etched. During the etching of silicon containing layers, it is desirable to measure etch rate, etch CD (critical dimension), etch profile, and etch uniformity from wafer to wafer or chamber to chamber. IR (infrared) absorption may be used to measure the concentration of a by-product produced by the etch process. 
     Current etch systems do not have means to measure in-situ fleet-wide, calibrated signals. In most cases, they rely on plasma emission that typically changes chamber to chamber and wet clean to wet clean, making it challenging to rely on signals to achieve in-situ metrology grade fault detection. Also, the signals that are currently available do not strongly depend on the on-wafer results, making it a weak predictor. 
     SUMMARY 
     To achieve the foregoing and in accordance with the purpose of the present disclosure, a method for processing a substrate in a processing chamber using at least one time trace based prediction model is provided. A substrate is dry processed, where the dry processing creates at least one gas by-product. A concentration of the at least one gas by-product is measured. A time trace of the concentration of the at least one gas by-product is obtained. The obtained time trace of the concentration is provided as input for the at least one time trace based prediction model to obtain at least one process output. The at least one process output is used to adjust at least one process parameter. 
     In another manifestation, a method to create a time trace based prediction model is provided. A plurality of substrates is dry processed, wherein the dry processing creates at least one gas by-product. A concentration of the at least one gas by-product is measured. Process parameters are recorded. A plurality of time traces of the concentration of the at least one gas by-product versus time for each substrate are obtained. Output parameters are measured. The process parameters, obtained plurality of time traces, and measured output parameters are used to create a time trace based prediction model of the at least one process output. 
     These and other features of the present disclosure will be described in more detail below in the detailed description of the disclosure and in conjunction with the following figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which: 
         FIG. 1A  is a high level flow chart of an embodiment. 
         FIG. 1B  is a flow chart of creating at least one time trace based prediction model. 
         FIG. 2  is a schematic view of a plasma processing chamber that may be used in an embodiment. 
         FIG. 3  is a more detailed schematic view of a gas cell of the embodiment, shown in  FIG. 2 . 
         FIG. 4  is a computer system that may be used in an embodiment. 
         FIGS. 5A-B  are time trace graphs. 
         FIG. 6  is a schematic view of a recurrent neural network model used in an embodiment. 
         FIG. 7  is a schematic illustration of how a plurality of time traces may be used to create a transformation matrix or coefficients of the regression, for a prediction model. 
         FIG. 8  is a plot of measured Predicted CD to Measured CD. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The present disclosure will now be described in detail with reference to a few preferred embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art, that the present disclosure may be practiced without some or all of these specific details. In other instances, well known process steps and/or structures have not been described in detail in order to not unnecessarily obscure the present disclosure. 
     Current technology used for process control (e.g., endpoint) relies on relative measurements or indirect measurements of plasma parameters using emission spectroscopy, reflectance, or RF (radio frequency) voltage and current. For endpoint control, optical emission spectroscopy reaches it limits with signal changes tending to zero when CDs shrink below 21 nm and aspect ratio increases beyond 30:1. For in-situ etch rate (ER) measurements, using RF voltage/current are based on correlations that are not always maintained chamber to chamber. 
     An embodiment relies on absolute measurements of SiF 4  or SiBr 4 , or SiCl 4  or other SiX 4  by-products that are a direct by-product of most silicon containing etches (nitrides, oxides, poly, and silicon films) when using fluorocarbon based chemistries. By combining the measurements with a time trace correlation, one can predict endpoint, ER as a function of depth, average wafer selectivity, and uniformity in certain conditions. The SiF 4  by-products are detected using IR absorption using quantum cascade laser spectroscopy allowing parts per billion level detection for accurate predictions. 
     This disclosure describes a method that combines time trace correlation coupled with SiF 4  IR-absorption to control the etch process. The method allows the extension of endpoint capability beyond the reach of tradition methods, such as emission spectroscopy, in high-aspect ratio applications such as DRAM cell-etch and 3D-NAND hole and trench patterning. The combination of absolute density measurement and time trace correlation allows one to additionally determine in-situ etch output parameters such as ER, selectivity, CD, and uniformity that can be used to achieve run-to-run process matching. 
     In an embodiment, an etch process is characterized by measuring a direct stable by-product that can be: 1) Used to determine endpoint for high-aspect ratio DRAM and 3D-NAND etches for process/CD control, 2) Used to scale endpoint detection for future nodes, 3) Combined with a time trace correlation, to determine in-situ: a) Average wafer ER and ER as function of depth (ARDE), b) An average wafer uniformity and selectivity, where both measurements can be used for run-to-run matching and fault detection, 4) Used with a high sensitivity quantum cascade laser spectroscopy to achieve ppb level limit of detection needed for accurate etch endpoint and etch parameters estimation. 
     To facilitate understanding,  FIG. 1A  is a high level flow chart of a process used in an embodiment. At least one time trace based prediction model is created (step  104 ). A substrate is dry processed (step  108 ). During the dry processing, a gas by-product is created. The concentration of the gas by-product is measured (step  112 ). A time trace of the measured concentration of the gas by-product is obtained (step  116 ). The time trace is provided as input for the at least one time trace based prediction model to obtain at least one process output (step  120 ). The at least one process output is used to adjust at least one process parameter (step  124 ). 
     EXAMPLES 
     In an example of an exemplary embodiment, at least one time trace based prediction model is created (step  104 ).  FIG. 2  schematically illustrates an example of a plasma processing chamber  200 , which may be used to create the at least one time trace based prediction model. In various embodiments, several plasma processing chambers  200  may be used in creating the at least one time trace based prediction model. The plasma processing chamber  200  includes a plasma reactor  202  having a plasma processing confinement chamber  204  therein. A plasma power supply  206 , tuned by a match network  208 , supplies power to a TCP coil  210  located near a power window  212  to create a plasma  214  in the plasma processing confinement chamber  204  by providing an inductively coupled power. The TCP coil (upper power source)  210  may be configured to produce a uniform diffusion profile within the plasma processing confinement chamber  204 . For example, the TCP coil  210  may be configured to generate a toroidal power distribution in the plasma  214 . The power window  212  is provided to separate the TCP coil  210  from the plasma processing confinement chamber  204  while allowing energy to pass from the TCP coil  210  to the plasma processing confinement chamber  204 . A wafer bias voltage power supply  216  tuned by a match network  218  provides power to an electrode  220  to set the bias voltage on the substrate  264  which is supported by the electrode  220 . A controller  224  sets points for the plasma power supply  206 , gas source/gas supply mechanism  230 , and the wafer bias voltage power supply  216 . 
     The plasma power supply  206  and the wafer bias voltage power supply  216  may be configured to operate at specific radio frequencies such as, for example, 13.56 MHz, 27 MHz, 2 MHz, 60 MHz, 200 kHz, 2.54 GHz, 400 kHz, and 1 MHz, or combinations thereof. Plasma power supply  206  and wafer bias voltage power supply  216  may be appropriately sized to supply a range of powers in order to achieve desired process performance. For example, in one embodiment, the plasma power supply  206  may supply the power in a range of 50 to 5000 Watts, and the wafer bias voltage power supply  216  may supply a bias voltage in a range of 20 to 2000 V. For a bias voltage up to 4 kV or 5 kV a power of no more than 25 kW is provided. In addition, the TCP coil  210  and/or the electrode  220  may be comprised of two or more sub-coils or sub-electrodes, which may be powered by a single power supply or powered by multiple power supplies. 
     As shown in  FIG. 2 , the plasma processing chamber  200  further includes a gas source/gas supply mechanism  230 . The gas source/gas supply mechanism  230  is in fluid connection with plasma processing confinement chamber  204  through a gas inlet, such as a shower head  240 . The gas inlet may be located in any advantageous location in the plasma processing confinement chamber  204 , and may take any form for injecting gas. Preferably, however, the gas inlet may be configured to produce a “tunable” gas injection profile, which allows independent adjustment of the respective flow of the gases to multiple zones in the plasma process confinement chamber  204 . The process gases and by-products are removed from the plasma process confinement chamber  204  via a pressure control valve  242  and a pump  244 , which also serve to maintain a particular pressure within the plasma processing confinement chamber  204 . The gas source/gas supply mechanism  230  is controlled by the controller  224 . A Kiyo® tool made by Lam Research Corp. of Fremont, Calif., may be used to practice an embodiment. In other examples, a Flex™ tool made by Lam Research Corp. of Fremont, Calif., may be used to practice an embodiment. 
     In this embodiment, connected to an exhaust pipe  246  after the pump  244 , a gas cell  232  is provided, into which exhaust gas flows. An IR light source  234  is positioned adjacent to a window in the gas cell  232 , so that an IR beam from the IR light source  234  is directed into the gas cell  232 . The IR beam can travel through the gas cell  232  multiple times (typically &gt;1 m) to achieve ppb level or even lower hundredth of ppt detection limits, so that the gas cell  232  is a multi-pass gas cell. The IR light is absorbed by the gas as it travels inside the gas cell  232 . An IR detector  236  is positioned adjacent to another window in the gas cell  232  to measure the light absorption level. 
       FIG. 3  is a more detailed schematic view of the gas cell  232  of the embodiment, shown in  FIG. 2 . The exhaust pipe  246  extends from the output of pump  244 . The gas cell  232  comprises a gas chamber  304 , a first mirror  308 , and a second mirror  312 . The gas chamber  304 , the first mirror  308 , and the second mirror  312  define an optical cavity  316 . The exhaust pipe  246  causes exhaust to flow into the optical cavity  316  in the gas chamber  304  and then out of the optical cavity  316  through an output port  320 . In this embodiment, the flow of the exhaust into and out of the optical cavity  316  is along a linear path. An IR light source  234 , which in this embodiment is a quantum cascade laser (QCL) IR light source, is provided adjacent to a window  328  in the first mirror  308 . An output fiber  332  is optically connected between an IR detector  236  and the optical cavity  316  through the second mirror  312 . The light can be coupled directly into the gas cell  232  or through optical fibers. Heaters  336  are placed adjacent to the first mirror  308  and the second mirror  312 . One or more of the heaters  336  may have heat sensors. The heaters  336  may be electrically connected to and controlled by the controller  224  and may provide temperature data to the controller  224 . A first purge ring  340  with a first purge ring channel  342  and a second purge ring  344  with a second purge ring channel  346  are provided, which surround the gas chamber  304 . The first purge ring  340  is adjacent to the first mirror  308  and has a first purge gas input  348 . The second purge ring  344  is adjacent to the second mirror  312  and has a second purge gas input  352 . The first purge ring  340  and the second purge ring  344  are in fluid communication with the gas cell  232  and optical cavity  316  through a plurality of purge gas nozzles  356 . 
       FIG. 4  is a high level block diagram showing a computer system  400 , which is suitable for implementing a controller  224  used in embodiments. The computer system may have many physical forms ranging from an integrated circuit, a printed circuit board, and a small handheld device, up to a huge super computer. The computer system  400  includes one or more processors  402 , and further can include an electronic display device  404  (for displaying graphics, text, and other data), a main memory  406  (e.g., random access memory (RAM)), storage device  408  (e.g., hard disk drive), removable storage device  410  (e.g., optical disk drive), user interface devices  412  (e.g., keyboards, touch screens, keypads, mice or other pointing devices, etc.), and a communication interface  414  (e.g., wireless network interface). The communication interface  414  allows software and data to be transferred between the computer system  400  and external devices via a link. The system may also include a communications infrastructure  416  (e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices/modules are connected. 
     Information transferred via communications interface  414  may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface  414 , via a communication link that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, and/or other communication channels. With such a communications interface, it is contemplated that the one or more processors  402  might receive information from a network, or might output information to the network in the course of performing the above-described method steps. Furthermore, method embodiments may execute solely upon the processors or may execute over a network such as the Internet in conjunction with remote processors that shares a portion of the processing. 
     The term “non-transient computer readable medium” is used generally to refer to media such as main memory, secondary memory, removable storage, and storage devices, such as hard disks, flash memory, disk drive memory, CD-ROM, and other forms of persistent memory and shall not be construed to cover transitory subject matter, such as carrier waves or signals. Examples of computer code include machine code, such as produced by a compiler, and files containing higher level code that are executed by a computer using an interpreter. Computer readable media may also be computer code transmitted by a computer data signal embodied in a carrier wave and representing a sequence of instructions that are executable by a processor. 
       FIG. 1B  is a flow chart that is used to create at least one time trace based prediction model (step  104 ), in an embodiment. A substrate is provided in a plasma processing chamber  200  (step  144 ). The substrate is processed (step  148 ). Process parameters, which are recorded, are used to control the processing of the substrate (step  152 ). The concentration of a gas by-product is measured (step  156 ). When the processing is completed, the substrate is measured in a metrology tool (step  160 ), which provides measured output parameters. The process is carried out for a plurality of substrates. The process parameters, measured concentrations, and measured output parameters are used to create a time trace based prediction model. A multi-variate method is used to create the time trace based prediction model, which correlates process parameters and measured output parameters with the time trace of the concentration of the gas by-product. The multi-variate method may use a neural network, such as a recurrent neural network (RNN) or a variant of RNN known as Long Short Term Memory neural networks (LSTM), or a multivariable analysis, such as partial least squares (PLS). Such a model would use not just a magnitude or a slope of the time trace of the concentration, but the dynamics of the time trace of the concentration, which includes magnitude, ratio of magnitude, curvature, lump sum of absorption values, and slope over the full time trace. In some embodiments, the dynamics of the time trace also uses the phase space of the time trace [s(t), s′(t)]. 
     In an example, a full time trace s(t) of the concentration of by-product is graphed with respect to time.  FIG. 5A  is a graph of s(t)  504 . In addition, the derivative of s(t) with respect to time s′(t) is also graphed  508 .  FIG. 5B  is a two dimensional phase space graph of s(t) versus s′(t) of the time trace shown in  FIG. 5A .  FIG. 6  is a schematic view of a Neural Network (NN) with memory that is used in this embodiment. The NN is provided different types of data  604 , which, in this example, falls in three categories, which are by-product time trace, process parameters, and measured output parameters. Process parameters are process inputs, which may include gas flow, RF power, bias voltage, or chamber characteristics, which influence process output. Measured output parameters may include etch rate, uniformity, CD, etch profile, wet clean, etch performance, or chamber cleaning performance. The data is provided to the input layers of the NN in various layers, the NN is a simple feedforward with 3 hidden layers connecting input time traces, process input to the output layer predicting wafer parameters. The different layers model different attributes of the by-product concentration time trace. For example, L 1  may be the magnitude of the concentration, L 2  may be the curvature of the time trace, and L 3  may be the ratio of adjacent magnitudes of the time trace. The NN produces a predictive model, which is used to predict an output  608 , which, in this example, may predict CD. In various embodiments, a plurality of time trace based prediction models may be created and/or used. For example, a time trace based prediction model of CD may be created and used and a time trace based prediction model of etch rate may be created and used. In an example, N time traces may be used to create a model with M outputs, where the M outputs may be CD, etch rate, or electrical yield and where N&gt;&gt;M. To provide a greater number of outputs would involve a greater number of layers or components of the PLS or NN. Therefore, a prediction model may have two, three, or even more process outputs. 
     The model outputs such as CD/ER may be used to create upper and lower limits to monitor in-situ process performance or used to provide a feedback for an advanced process control algorithm to adjust process parameters for the subsequent substrates. During the processing of a substrate, a time trace may be used to determine whether a process is exceeding the upper or lower limits of the CD or the upper or lower limits of the ER. 
     In another example, a PLS method is used to correlate the CD to a part or the full SiF 4  time evolution in order to create a time trace based prediction model.  FIG. 7  is a schematic illustration of how a plurality of time traces may be used to create a transformation matrix or coefficients of the regression, for a prediction model. Data from the plurality of time traces is provided in a matrix (X). The resulting wafer features are also placed in a matrix (F). A transformation matrix (β) may be calculated using a partial least squares method such that F=βX+ε, where E is an error term.  FIG. 8  compares the predicted CD versus the measured CD. In this example, multiple SiF 4  time traces are recorded for different ER conditions leading to different etch feature CD outputs on the wafer. A model is generated by regressing the CD values onto the SiF 4  time traces. The resulting correlation between the model simulated CD and the measured one is better than 98%. 
     A dry process is performed on the substrate (step  108 ) in the processing chamber, where the dry process creates at least one gas by-product. In different embodiments, either the substrate is a silicon wafer, which is etched or one or more silicon containing layers over the substrate are etched. In this example, a stack of alternating silicon oxide and silicon nitride layers is etched. Such an alternating stack of silicon oxide and silicon nitride is designated as ONON, which is used in 3D memory devices. In this example, there are at least eight alternating layers of ONON. In etching such a stack, both ER and selectivity decrease with aspect ratio, meaning that the difference between etch rates of the silicon oxide and silicon nitride decreases as aspect ratio, the ratio of the etch depth over the etch width, increases. To etch such a stack, an etch gas of C x F y H z /O 2  is provided by the gas source/gas supply mechanism  230 . RF power is provided by the plasma power supply  206  to the TCP coil  210  to form the etch gas into an etch plasma, which etches the stack and forms at least one gas by-product, which in this example is SiF 4 . (Other etch by-products such as SiBr 4  or SiCl 4  can be monitored depending on the gas chemistry by tuning the IR light source to the absorption band of each by-product.) 
     During the dry process, the concentration of the at least one gas by-product is measured over time (step  112 ). In this embodiment, exhaust from the pump  244  flows to the gas cell  232 . The IR light source  234  provides a beam of IR light into the gas cell  232 . The first mirror  308  and the second mirror  312  reflect the beam of IR light a plurality of times before the beam of IR light is directed to the IR detector  236 , which measures the intensity of the beam of IR light. The optical path length of the IR beam can reach few meters to few hundreds of meters, thus allowing for sub ppb detection limit. In an embodiment, the optical path is at least one meter. Data from the IR detector  236  is sent to the controller  224 , which uses the data to determine the concentration of the SiF 4 . 
     At the completion of the etch, a time trace is obtained for the time evolution of the concentration of etch by-product (step  116 ). The time trace is then used as input for the prediction model to determine on-wafer process result or process output (step  120 ), such as CD. The process output is used to adjust at least one process parameter (step  124 ). For example, the CD values can be fed to a host for advanced process control, allowing it to feedforward gas flow, power or time to adjust for the next substrate or next lot of substrates CD output. In one example, the process output may show that the CD is above a set threshold. As a result, a process parameter may be instantly changed to correct the CD. In one example, the O 2  flow is tuned to adjust the ratio of etch to polymer deposition. In another embodiment, a process parameter may be adjusted after the process is completed to change the process for the next substrate. 
     In this example, a relationship between a process parameter and by-product concentration has been determined with a high degree of accuracy and without sophisticated analysis or modeling. Such a relationship was not previously known, so that previously an expensive and time consuming metrology process was required in order to determine CD. In an example, after few wafers are processed and measured by metrology, a model may be provided between the time trace of absorbance and the CD outputs. When the model is established and confirmed, the CD outputs at the end of the wafer process can be predicted, without waiting for the metrology tool feedback. Waiting for metrology tool feedback can take multiple hours before the data is available, which may result in multiple lot scrap. Here, by having the reliable CD model in real time, the process can be adjusted and wafer to wafer control is achieved. The process allows for the process outputs to be provided in real time, so that adjustments may be made in real time. Such real time adjustments are defined as either being made while the wafer being measured is still being processed, or before the next wafer is processed, without a pause between wafers to provide analysis. 
     In other embodiments, time trace based prediction models between time trace and bottom CD, top CD, etch rate, feature bowing, yield rate, wafer auto clean (WAC) process, etch uniformity, wet clean parameters, chamber clean parameters, or chamber features may be determined. The time trace based prediction models may be used to establish upper and lower thresholds or limits. When the limits are exceeded, either parameters for a current dry process may be changed to facilitate the current dry process, or a parameter may be changed for a future dry process. One example of a parameter change for a future process may result from a determination that an etch profile is out of specification to an extent that a chamber needs to be adjusted or cleaned. The comparison of the time trace with the time trace based prediction model would be used to indicate how the chamber needs to be adjusted. For example, the comparison may indicate that the chamber needs a wet cleaning. As a result, the chamber would be subjected to a wet clean. Such a determination is made, during the dry processing of a substrate, without requiring performing an expensive and time consuming metrology on the substrate and without processing several substrates out of specification, before determining that a cleaning is needed. The comparison may be used for advanced fault detection to determine run-to-run and chamber-to-chamber performance. An embodiment may use the time traces as input for time trace based prediction models during the processing of a plurality of substrates in succession to measure the chamber drift from specification to determine an optimal time for readjusting or reconditioning the chamber. 
     In various embodiments, the dynamics of a time trace are used. The use of the dynamics of a time trace uses more than just a magnitude versus time. Such dynamics would also use the time derivative of the time trace. More preferably, such dynamics also use curvature of the time trace. Most preferably, such dynamics also use ratios of magnitudes and other features. The dynamics may also use one dimensional or two dimensional phase space graphs of the time trace. 
     In an experiment, a measured absorption had two contributors: the wafer and the chamber. Three-fourths of the absorption was attributed to the chamber since it has the largest area in contact with the plasma. The other one-fourth was attributed to the wafer. By assuming that the wafer attribution is constant over time or has a very small variation, it can be assumed that any change over time is due to a change in the chamber attribution. The change may be attributed to parts erosion. This embodiment provides an indication in real time of chamber part erosion. 
     In another embodiment, the level of absorbance is measured during a waferless auto clean (WAC). Again, there are two respective contributions from the polymer and the chamber. Near the end of WAC, the chamber is clean, so the only contribution left is the contribution from the chamber. Monitoring the value of absorbance at the end of each WAC over wet cleans will provide the chamber clean fingerprint. The absorption in the foreline is where the measurement is made. 
     It has been unexpectedly found that such methods are successful when concentrations can be measured with an accuracy of parts per billion (ppb) and more preferably parts per trillion (ppt). For low pressure plasma processing systems, it has been found that a post exhaust gas cell, which measures absorption after light has passed through the gas cell a plurality of times to provide a light path of greater than 1 meter provides the required accuracy. Advantages of placing the gas cell after the exhaust pump are that the gas is denser after the exhaust pump than the gas in the processing chamber. In addition, reflective surfaces are not exposed to the plasma in the processing chamber, so that reflective surfaces would not be degraded by plasma radicals or ions. In other embodiments, the gas cell is in the plasma processing chamber, such as surrounding the plasma region where the plasma is at a higher pressure. In other embodiments, visible light or UV light may be used instead of IR light. 
     Various embodiments are useful for etching memory devices such as DRAM and 3D-NAND devices. In various embodiments, the plasma process is an etch process of a silicon containing layer or a low-k dielectric layer. In various embodiments, the RF power may be inductively coupled or capacitively coupled. A Flex™ tool made by Lam Research Corp. of Fremont, Calif., may be used to practice an embodiment with capacitive coupling to etch DRAM and 3D NAND structures. In other embodiments, other types of plasma power coupling may be used. In other embodiments, alternating layers of silicon oxide and polysilicon (OPOP) may be etched. 
     While this disclosure has been described in terms of several preferred embodiments, there are alterations, permutations, modifications, and various substitute equivalents, which fall within the scope of this disclosure. It should also be noted that there are many alternative ways of implementing the methods and apparatuses of the present disclosure. It is therefore intended that the following appended claims be interpreted as including all such alterations, permutations, and various substitute equivalents as fall within the true spirit and scope of the present disclosure.