HOVER SURVEILLANCE USING A MULTI-PHASE EQUIPMENT METHOD

Abstract

A capacity measurement device (100) comprising the following: a transmit (TX) generator (215) configured to generate a sequence of receive (RX) signals by applying each TX signal pattern (420) in a sequence of TX signal patterns (400) to a set of sensor electrodes (1-24), wherein the TX generator (215) is configured for each TX signal pattern (420) in the sequence of TX signal patterns (400), to apply a first excitation signal and a second excitation signal to each subset of a plurality of subsets, each comprising three or more connected sensor electrodes of the set of sensor electrodes (1-24), wherein the plurality of subsets includes at least half of the sensor electrodes in the set of sensor electrodes (1-24); a sequencer circuit (223) coupled to the TX generator (215) and configured to determine, for each TX signal pattern (420) in the sequence of TX signal patterns (400), a next subsequent TX signal pattern in the sequence based on a circular rotation of the TX signal pattern; and a processing block (222) coupled to the TX generator (215) and configured to detect the presence of an object near the set of sensor electrodes (1-24) based on a correlation measure between the sequence of RX signals and a predefined function.

Description

RELATED PATENT APPLICATIONS

[0001] This patent application is an international patent application of the non-provisional US patent application No. 16 / 016,194 filed on June 22, 2018, claiming the priority and benefit of the provisional US patent application No. 62 / 598,347 filed on December 13, 2017. AREA OF INVENTION

[0002] This disclosure relates to the field of capacity measurement and, in particular, the measurement of hover inputs on capacitive touch sensing surfaces. STATE OF THE ART

[0003] Computing devices, such as notebook computers, personal data assistants (PDAs), kiosks, and mobile phones, feature user interface devices, also known as HIDs (Human Interface Devices). One type of user interface device is a touch-sensitive pad (also commonly called a touchpad), which can be used to emulate the function of a personal computer's (PC) mouse. A touch-sensitive pad replicates the X / Y movement of a mouse by using two defined axes containing a collection of sensor electrodes that detect the position of one or more objects, such as a finger or stylus. The touch-sensitive pad provides a user interface device for performing functions such as positioning a pointer or selecting an item on a display.Another type of user interface device is a touchscreen. Touchscreens, also known as touch windows, touch panels, or touchscreen panels, are transparent display overlays that allow a display to be used as an input device, thus eliminating the need for the keyboard and / or mouse as the primary input device for interacting with the display's content. Other user interface devices include buttons, sliders, and other elements that can be used to detect touches, taps, drags, and other gestures.

[0004] Capacitance sensing systems are increasingly used to implement these and other types of user interface devices. These systems operate by detecting electrical signals generated at electrodes that reflect changes in capacitance. Such changes in capacitance might indicate a touch event or the presence of a conductive object, such as a finger, near the electrodes. The capacitance changes of the sensing electrodes can then be measured by an electrical circuit that converts the capacitances measured at the capacitive sensing elements into digital values ​​for interpretation by a host device. However, the accuracy of existing capacitance sensing circuits can be degraded by noise and fluctuations affecting the drive voltages, current source outputs, switching frequencies, and other signals within the sensing circuit.Such measurement inaccuracies can result in inaccurate positioning or touch detection in a capacitance-based user interface device.

[0005] Furthermore, the disclosures in the following publications may be helpful for understanding the present invention: US 2016 / 0041210A1, US 2017 / 0212635A1, US 2012 / 0223911A1, and US 2011 / 0007021A1. The circuit in US 2016 / 0041210A1 utilizes multi-phase transmit (TX) scans, in which several electrodes are simultaneously driven with different phases (e.g., +1, -1, 0). This results in convoluted capacitance data containing the measured signals. A digital deconvolution circuit extracts the data by multiplying and summing it with the inverse values ​​of the TX patterns, thereby significantly improving the performance, efficiency, and accuracy of capacitive touch systems.

[0006] US 2017 / 0 212 635 A1 deals with capacitive touch systems and improves the signal quality through a time-variable adjusted gain and transfer function that behaves synchronously with the transitions of the response signal and thus effectively suppresses noise and interference.

[0007] The subject of US patent 2012 / 0223911A1 reduces periodic noise in capacitive touch sensors by detecting the phase of the noise signal and phase-locking the excitation voltage accordingly. This converts the noise into a constant offset component that can be easily detected and compensated for in a calibration process.

[0008] US patent 2011 / 0007021A1 concerns the improvement of capacitive touch and hover detection using a sensor matrix. This results in a more precise determination of the position of an object located near the surface – even outside the direct center, for example, at the edge of a touchscreen. BRIEF DESCRIPTION OF THE FIGURES

[0009] The present revelation is illustrated in the figures of the accompanying drawings in an exemplary and non-limiting manner. Fig. Figure 1 is a block diagram illustrating a capacity measurement system according to one embodiment. Fig. Figure 2 is a block diagram illustrating components in a processing device of a capacity measurement system according to one embodiment. Fig. Figure 3 illustrates circuit diagrams for stages of a multiphase detection process according to one embodiment. Fig. 4A and Fig. Figure 4B illustrates sequences of excitation signal patterns for a hover detection process according to one embodiment. Fig. 5A and Fig. Figure 5B illustrates hover inputs at different locations of a sensor array and the measured signals corresponding to the hover inputs according to one embodiment. Fig. Figures 6A-6C illustrate least-squares approximations of measured signal sequences using sine functions according to one embodiment. Fig. Figure 7 illustrates a displacement of a sine function according to one embodiment. Fig. Figure 8 illustrates a subdivision of a sine function period into quarters for reference to a lookup table according to one embodiment. Fig. 9A and Fig. Figure 9B illustrates, according to one embodiment, an approximation of a subset of signals surrounding a maximum measured value with a parabola. Fig. Figures 10A-11 illustrate approximations of different measured signal sequences by reciprocal quadratic curves according to one embodiment. Fig. Figure 12 illustrates, according to one embodiment, the calculation of a hover input position based on differentials calculated from a measured signal sequence. Fig. Figures 13A-13C illustrate, according to one embodiment, sequences of excitation signal patterns, wherein a sensor electrode is measured for each pattern. Fig. Figures 14A-14C illustrate, according to one embodiment, sequences of excitation signal patterns applied to both row and column sensor electrodes. Fig. Figure 15 illustrates, according to one embodiment, measured signal sequences resulting from the excitation of both row and column sensor electrodes using different excitation patterns. Fig. Figure 16 illustrates a process for detecting the presence and location of a hover input according to one embodiment. Fig. Figure 17 illustrates a process for detecting the presence and location of a hover input according to one embodiment. DETAILED DESCRIPTION

[0010] The following description sets forth numerous specific details, such as examples of specific systems, components, methods, and so forth, to provide a good understanding of various embodiments of the claimed subject matter. However, it will be clear to a person skilled in the art that at least some embodiments can be carried out without these specific details. In other cases, well-known components or methods are not described in detail or are presented in a simple block diagram format to avoid unnecessarily obscuring the claimed subject matter. The specific details set forth are therefore merely exemplary. Certain implementations may differ from these exemplary details and nevertheless be considered to be within the spirit and scope of the claimed subject matter.

[0011] In a computing device capable of accepting input via a capacitive sensing surface, such as a touchscreen or trackpad, hover input is caused by an object (such as a user's finger or stylus) that is near the sensing surface but not touching it. For example, hover input can be detected when a user's finger is held over the touch sensing surface. The computing device determines the point on the sensing surface closest to the finger as the input point. A computing device capable of hover detection can detect input from a user even when the user is unable or unwilling to physically touch the device, such as when the user is wearing gloves, the user's fingers are wet or dirty, the device is in a protective case, etc.A hover input at a specific location on the capture surface can be distinguished from a touch input at the same location; accordingly, hover capture can be used to perform additional types of actions in the computing device (e.g., previewing documents, opening submenus, etc.) that are not initiated by touch input.

[0012] In one embodiment, a computing device performing hover detection is capable of detecting a finger at a distance of at least 35 millimeters from the detection surface. For example, at this distance, a typical capacitance between a finger and an indium tin oxide (ITO) sensor electrode can be approximately 15–30 femtofarads (fF), depending on the properties of the finger and the electrode and their relative positions. Therefore, hover detection is effectively performed using low-noise, high-resolution charge-to-digital conversion circuits capable of detecting minute capacitance changes in the presence of relatively large parasitic capacitances. Temperature drift in the large parasitic capacitances, in combination with internal and external noise sources (e.g.,Noise from a liquid crystal display (LCD) significantly hinders the detection of hover inputs and can result in false inputs, failure to detect actual hover inputs, position jitter, and other operational artifacts. Additionally, emission limits imposed in some industries (e.g., automotive and aerospace applications) restrict the dimensions of sensing surfaces and the magnitude of drive signals applied to sensor electrodes to perform hover detection operations.

[0013] In one embodiment, a computing device detects hover inputs on a sensing surface by applying a sequence of zero-sum patterns of excitation signals to an array of sensor electrodes in the sensing surface. The device applies complementary transmit (TX) excitation signals (i.e., a positive phase and a negative phase of an excitation signal) to the sensor electrodes, with positive and negative phase signals being applied to equal numbers of electrodes such that emissions induced by the excitation signals cancel each other out at a sufficient distance from the sensor electrodes.

[0014] In one embodiment, an initial TX signal pattern comprises positive-phase excitation signals applied to a subset of adjacent sensor electrodes, while negative-phase excitation signals are applied to a complementary subset of sensor electrodes. The application of subsequent TX signal patterns in the sequence causes the initial excitation signal pattern to rotate circularly through the sensor electrodes. The application of the sequence of TX excitation signal patterns causes the generation of a sequence of receive (RX) signals at the sensor electrodes, which is influenced by hover inputs near the sensor electrodes.

[0015] A least-squares approximation is performed to determine parameters for a predefined function used to approximate the measured sequence of RX signals. In one embodiment, a hover input near the sensor electrodes results in a sequence of RX signals resembling a sine function; accordingly, the least-squares approximation determines the parameters of the sine function used to approximate the sequence of RX signals. The position of the hover input relative to the sensor electrodes is determined by calculating a displacement parameter for the sine function that approximates the measured RX signal sequence.

[0016] A correlation coefficient is calculated that describes the degree of correlation between the measured RX signal sequence and the sine function that approximates the RX signal sequence. Actual hover inputs, caused by a finger or other conductive object near the sensor electrodes, result in the measurement of an RX signal sequence that resembles a sine function, while signals caused by temperature changes or other unwanted inputs do not. Therefore, the correlation coefficient is compared to a correlation threshold, and a hover input is detected when the correlation coefficient exceeds the threshold (i.e., indicates a sufficiently high degree of similarity between the measured RX signal sequence and the approximating sine function).

[0017] The above approach to detecting a hover input results in a significant increase in the measurable capacitance signal derived from the hover input. The signal-to-noise ratio is also improved. Consequently, hover inputs are detectable at greater distances from the sensing surface, with greater immunity to noise (e.g., from a nearby LCD panel). Additionally, measurement inaccuracies due to changes in parasitic capacitances of the sensing surface (e.g., due to temperature drift) are significantly reduced, as these changes are compensated for by the zero-sum TX excitation signal patterns. Lower electromagnetic emissions are also achieved through the zero-sum TX excitation signal patterns.

[0018] Fig. Figure 1 illustrates a block diagram of a capacitance sensing system 100 according to one embodiment. The sensing system 100 comprises a host device 101, a processing device 102, and a sensor array 103. In one embodiment, the sensor array 103 is made of a transparent, conductive material such as indium tin oxide (ITO) and rests on a display 104. The host device 101 controls the display 104 and updates it in response to objects detected based on intrinsic and counter-capacitances measured at the sensor array 103, so that the display 104 and the sensor array 103 together function like a touchscreen. In one embodiment, the functions of the processing device 102 are integrated with the host device 101 into a single integrated circuit.

[0019] Fig. Figure 2 illustrates a functional block diagram of components in the capacitance sensing system 100 that perform hover input detection according to one embodiment. The processing device 102 is capable of measuring signals (e.g., voltage and / or current) representing intrinsic and counter-capacitances of electrodes in the capacitive sensor array 103. The sensor array 103 comprises a set of one or more TX sensor electrodes and a set of one or more RX sensor electrodes. Each of the TX sensor electrodes is connected to the processing device 102 via one of the TX lines 211, while each of the RX sensor electrodes is connected to the processing device 102 via one of the RX lines 212. In countercapacitance detection, the TX lines 211 are used to apply excitation signals to the TX sensor electrodes, while the RX lines 212 are used to receive the induced signals from the RX sensor electrodes.In other operating modes (e.g., multiphase self-capacitance sensing), one or both of the TX lines 211 and the RX lines 212 can also be configured to transmit signals to and / or receive signals from the sensor electrodes when capacitance values ​​are measured. The processing device 102 processes the measured capacitance values ​​to distinguish between intended and unintended contacts (e.g., liquid on the sensor surface) and to determine the locations of intended contacts. Based on RX signal sequences generated at the sensor array 103, the processing device 102 also detects hover inputs. The host device 101 responds to a detected hover input by executing a subroutine associated with the hover input. In one embodiment, the executed subroutine is additionally determined based on the location of the hover input.In one embodiment, the host device 101 executes different subroutines for a hover input and a deliberate touch input at the same location.

[0020] The processing device 102 reports the locations of intended touches and hover inputs to the host device 101. Based on the reported input locations, the host device 101 executes one or more functions. In one embodiment, the processing device 102 reports the measured signals, which represent changes in intrinsic and / or counter-capacitances, to the host device 101, and further processing of the measured values ​​is performed in the host device 101. The processing device 102 can report the raw measured values ​​to the host 101 or can perform preliminary processing on the raw measured values ​​(e.g., baseline correction or filtering) before reporting them to the host 101.

[0021] The processing device 102 comprises a number of components for supplying excitation signals to the sensor array 103, measuring the resulting signals (e.g., current or charge) at the sensor array, and calculating measures of intrinsic and anticapacitances (i.e., values ​​representing the intrinsic and anticapacitances) based on the measurements. The multiplexer 213 comprises switching circuits that selectively connect the different sensor electrodes to excitation signals or measurement channels. In one embodiment, the multiplexer 213 is implemented using a number of multiplexers. For example, separate multiplexers can be used for the TX lines 211 and for the RX lines 212, or for column and row electrodes. In one embodiment, several multiplexers can be used for each axis, depending on the number of inputs, the drive and receive configurations, and the dimensions of the multiplexers.The TX generator 215 controls the multiplexer 213 to generate a TX signal as an excitation signal, which is selectively applied to the TX sensor electrodes in the array 103 via the TX lines 211. A Vtx generator 214 generates a voltage Vtx, which can be selectively applied to the sensor electrodes when generating the TX excitation signal. In one embodiment, the Vtx generator 214 includes an inverter, so that both positive phase (+1) and negative phase (-1) signals can be generated. The multiplexer 213 can also selectively apply a ground voltage to the sensor electrodes. The specific sequences of excitation signals applied to the sensor array 103 over time are generated by the sequencer circuit 223.

[0022] The multiplexer 213 can also selectively connect the electrodes in the sensor array 103 to the charge-to-code converters 216, allowing the amounts of charge generated by the excitation of the electrodes to be measured. In one embodiment, the charge-to-code converters 216 integrate current over a set period and convert the resulting measured charge into a digital code that can be used for further processing. The charge-to-code converters 216 include at least one RX channel 224 for receiving an RX signal from the sensor array 103. The baseline compensation circuit 217 provides a baseline compensation signal to the capacitance-to-code converters 216, which reduces the effect of a baseline level from the sensor array. Alternatively, the baseline compensation circuit 217 can apply the compensation signal to a shielding electrode located beneath the sensor array 103.The compensation level can be set for the entire panel or can be set to different levels for different excitation signal patterns, depending on the parasitic capacitance fluctuations of the panel's sensing electrodes.

[0023] The channel engine 218 receives the digital codes representing the charge measured at each electrode. In some acquisition methods (e.g., multiphase self-capacitance acquisition with non-zero-sum TX excitation patterns), the channel engine 218 delivers the raw digital code values ​​to the deconvolution module 219. The deconvolution module 219 performs deconvolution operations on the values ​​to generate a countercapacitance map 220 and a self-capacitance vector 221. The countercapacitance map 220 is represented as a matrix of values ​​having dimensions corresponding to the number of row electrodes and column electrodes in the sensor array, such that the countercapacitance of each node between one of the row electrodes and one of the column electrodes is represented by an element (i.e., a measure of countercapacitance) in the matrix. The self-capacitance vector includes one element for each TX electrode (e.g.,line electrode), which represents the intrinsic capacitance of the TX electrode (i.e., a measure of intrinsic capacitance).

[0024] The counter capacities 220 and the own capacities 221 are transferred to the post-processing and communication block 222. The post-processing block 222 performs additional calculations to detect the presence of any intended contacts and to determine the locations of such contacts based on capacities 220-221. The contact locations are then transferred from block 222 to the host device 101.

[0025] In one embodiment, when the processing device 102 performs a hover capture, the generated sequence of RX signals is received by the channel engine 218 and stored in the intrinsic capacitance vector 221, bypassing the deconvolution module 219, which is used in other operating modes. The post-processing block 222 determines parameters of a sine function that approximates the RX signal sequence based on a least-squares approximation. In one embodiment, values ​​for the sine function are calculated using the lookup table 225. The post-processing block 222 calculates a correlation measure (e.g., a correlation coefficient) that indicates the degree of correlation between the RX signal sequence and the approximating sine function, and then, based on the correlation coefficient, detects the presence of an object (e.g., the user's finger) near the sensor array 103.The presence of the object is detected in particular when the correlation coefficient exceeds a predetermined correlation threshold (i.e., when the RX signal sequence is sufficiently similar to the approximating sine function).

[0026] Fig. Figure 3 illustrates two stages of operation of a capacitance sensing circuit 300, which performs multiphase self-capacitance sensing, according to one embodiment. The capacitance sensing circuit 300 measures the self-capacitances of two electrodes RX-1 and RX-N, which represent the first RX electrode and the Nth RX electrode in the sensor array 103. The capacitances Cs1 and CsN represent the self-capacitances of electrodes RX-1 and RX-N, respectively.

[0027] During the pre-charging stage, the sensing electrodes RX-1 and RX-N are isolated from the sensing channel 301 by opening switches SW3-1 and SW3-N. Accordingly, the sensing electrodes RX-1 and RX-N are isolated from each other and can be pre-charged to different voltages. In the sensor array 103 as a whole, some electrodes can be pre-charged to Vtx while others are pre-charged to ground. As illustrated, SW2-1 is closed while SW1-1 is open, so electrode RX-1 is connected to ground and RX-N is connected to Vtx. The intrinsic capacitances Cs1 and CsN are therefore pre-charged to ground and Vtx, respectively.

[0028] During the acquisition stage, switches SW2-1 and SW2-N are opened to disconnect sensor electrodes RX-1 and RX-N from their respective pre-charge voltages. Sensor electrodes RX-1 and RX-N are connected to acquisition channel 301 by closing switches SW3-1 and SW3-N. The voltage Vref is maintained at each electrode RX-1 and RX-N. A charge Q1 flows into the intrinsic capacitance Cs1 of electrode RX-1 because RX-1 was pre-charged to a lower voltage than Vref. A charge QN flows out of the intrinsic capacitance CsN because RX-N was pre-charged to a higher voltage Vtx than Vref. When this process is performed for all RX electrodes (RX-1, RX-2... RX-N) in sensor array 103, acquisition channel 301 receives a charge Qin according to Equation 1 below: Qin=Q1+Q2+⋯+QN;

[0029] In Equation 1, the values ​​(Q1, Q2, ... QN) represent the charge stored in the respective intrinsic capacities (Cs1, Cs2, ... CsN) after the pre-charging stage. Equation 1 can be rewritten as shown in Equation 2 below: Qin=S1⋅Utx⋅Cs1+S2⋅Utx⋅Cs2+⋯+SN⋅Utx⋅CsN==Utx⋅[S1 S2…Sn]⋅[Cs1Cs2⋮CsN]=Utx⋅S⋅Csx;

[0030] In Equation 2, (S1-Sn) represents the excitation sequence for one measurement cycle, represented by elements 1, -1, and 0. A value of 1 indicates excitation in a positive direction, a value of -1 indicates excitation in a negative direction, and a value of 0 indicates that no excitation voltage is applied to the sensor electrode. Accordingly, Utx represents the change in voltage applied to the electrode from the pre-charge stage to the sensing stage. As shown in Equation 2, Utx is the same for all electrodes; in alternative embodiments, Utx may differ from electrode to electrode.

[0031] If the sensor is successively excited with N different excitation sequences, the excitation procedure can be represented as an excitation matrix S with the values ​​S11-SNN, as shown in equation 3 below. Qin=Utx⋅⌊S11S21⋯SN1S12S22⋯S​N2⋮⋮⋱⋮S1NS2N⋯SNN⌋⋅⌊Cs1Cs2⋮CsN⌋=Utx⋅S⋅Csx;

[0032] In the excitation matrix S, elements in the same row (e.g., S11, S21,... SN1) are applied to different electrodes at the same time, while elements in the same column (e.g., S11, S12,... S1N) are applied to the same electrode at different times. If the excitation matrix S has an inverse form S -1 The recorded intrinsic capacities can be determined by performing a deconvolution of the measured charge values ​​Qin, as shown in equation 4 below, where D is the deconvolution matrix: Csx=⌊D11D21⋯DN1D12D22⋯DN2⋮⋮⋱⋮D1ND2N⋮DNN⌋⋅⌊Qin1Qin2⋮QinN⌋Utx=D⋅QinUtx;D=S−1;

[0033] Exciting the sensor electrodes with a combination of out-of-phase signals reduces sensor emissions compared to in-phase excitation of all row or column sensor electrodes. The emission depends on the sum of the excitation sequence elements (e.g., S11-SNN). If the sum of the elements is equal to 1, for example, the emission observed over a distance is similar to the emission produced by exciting a single electrode. Additionally, the deconvolution calculation includes charge measurements for multiple sensor electrodes, which, after deconvolution, results in an averaging effect that makes the detection result less sensitive to noise introduced into the sensor. In one embodiment, the approaches described herein for detecting hover inputs with multiple charge-in-code detection circuits (e.g.,different acquisition channels based on active integrators, power feeders, etc.) are used, which support multi-phase self- or counter-capacitance acquisition approaches.

[0034] Fig. Figure 4A illustrates a sequence of TX signal patterns applied to sensor electrodes in a sensor array during hover detection, according to one embodiment. As shown in Fig. As illustrated in Figure 4A, each of the sensor electrodes is referred to by its index 410. In one embodiment, electrodes 1-24 are row electrodes (or alternatively, column electrodes) in the sensor array 103, with the electrode indices 410 corresponding to the spatial order of the electrodes in the array 103. The TX generator 215 applies each of the TX signal patterns 420 to the sensor electrodes in the sensor array 103 in sequential order.

[0035] When each signal pattern 420(1)-420(24) is applied, the individual TX excitation signals in the pattern are simultaneously applied by the TX generator 215 to the respective sensor electrodes 1-24, with '+1' and '-1' indicating the positive and negative phases of the excitation signal, respectively. For example, the TX generator 215 applies pattern 420(1) to the sensor electrodes 1-24 by applying a positive-phase excitation signal to electrodes 1-12 while applying a negative-phase excitation signal to electrodes 13-24. In one embodiment, the positive and negative-phase signals are complementary signals, with the negative-phase excitation signal being the inverse of the positive-phase excitation signal.

[0036] Each of the signal patterns 420 is a zero-sum excitation signal pattern; that is, the magnitude of the sum of the excitation signals in the pattern ideally approaches zero. For example, excitation signal pattern 420(1) comprises twelve positive-phase excitation signals (applied to electrodes 1-12) and twelve negative-phase excitation signals (applied to electrodes 13-24). Therefore, each positive-phase excitation signal is summed with a corresponding complementary negative-phase excitation signal, so that the sum of all signals in the pattern is nominally zero. In practice, the sum of the excitation signals in the pattern may deviate from zero due to manufacturing tolerances, environmental conditions, etc.

[0037] In one embodiment, the sum of the excitation signals in a pattern is smaller than the magnitude of any single excitation signal in the pattern. Accordingly, the entire zero-sum excitation signal pattern produces less electromagnetic emission than applying any single excitation signal to its corresponding sensor electrode. In another embodiment, the sensor array comprises an even number of sensor electrodes, and the positive and negative phase excitation signals are each applied to half of the sensor electrodes, such that an equal number of electrodes are excited by the positive phase signal as by the negative phase signal, thus achieving a balanced excitation scheme.

[0038] For each of the excitation signal patterns 420, the TX generator 215 applies all of the positive-phase excitation signals to a contiguous subset of sensor electrodes, which are connected in a circular sequence. The circular sequence of electrodes proceeds sequentially from index 1 to index 24, then returns to index 1 after index 24, and repeats. Accordingly, in signal pattern 420(24), electrodes 24 and 1-11, to which the positive-phase excitation signals are applied, are connected in the circular sequence. All of the negative-phase excitation signals are also applied to a contiguous subset of electrodes, which are connected in the circular sequence.

[0039] In one embodiment, the same excitation signal is applied to a subset of sensor electrodes by physically connecting the electrodes via a conductive path (e.g., through switching circuits) and applying the excitation signal to the connected electrodes. In an alternative embodiment, each of the sensing electrodes is driven separately with the same excitation signal. In one embodiment, a majority of the electrodes (i.e., at least half of the electrodes) in the set of sensor electrodes are comprised of subsets, each comprising at least three or more connected sensor electrodes, to which the same excitation signal (e.g., the positive-phase excitation signal or the negative-phase excitation signal) is applied for each signal pattern.

[0040] A circular rotation is used to generate successive signal patterns in sequence 400. In sequence 400, twelve positive-phase excitation signals and twelve negative-phase excitation signals rotate circularly through the sensor electrodes to form 24 distinct excitation signal patterns 420. For each signal pattern 420 in sequence 400, the sequencer circuit 223 generates the next subsequent excitation signal pattern in the sequence by rotating the pattern circularly by an increment of one; that is, each sensor electrode in the next subsequent pattern receives the excitation signal that was applied to a preceding sensor electrode (i.e., one with a lower index) in the circular sequence.For example, in pattern 420(2), a negative-phase excitation signal is applied to electrode 1 because the negative-phase excitation signal in the previous excitation signal pattern 420(1) was previously applied to electrode 24 (which precedes electrode 1 in the circular sequence). In pattern 420(2), a positive-phase excitation signal is applied to electrode 13 because a positive-phase excitation signal was applied to electrode 12 in the preceding pattern 420(1).

[0041] Fig. Figure 4B illustrates an alternative sequence 430 of circularly rotating TX signal patterns that can be applied to sensor electrodes in a sensor array during hover detection, according to one embodiment. The TX generator 215 applies each of the TX signal patterns 440 sequentially to the sensor electrodes in the sensor array 103. In the signal patterns 440, the sensor electrodes to which the positive (or negative) phase excitation signal is applied are divided into two groups. In pattern 440(1), the positive phase excitation signal is applied to electrodes 1-6 and 13-18, while the negative phase excitation signal is applied to electrodes 7-12 and 19-24.

[0042] When other non-zero-sum TX excitation pattern sequences are used for capacitive multiphase sensing, deconvolution can be used to determine the measured capacitance values ​​from the raw data. However, the convolution matrices for zero-sum signal patterns, such as patterns 420 and 430, are singular. Therefore, instead of deconvolution, a least-squares approximation calculation is performed to match a predefined function (e.g., a sine function) to the measured sequence of RX signals in order to determine the presence and location of a hover input relative to the sensor electrodes.

[0043] In one embodiment, after the pre-charge phase in which the excitation signal of the positive or negative phase is applied to the sensor electrode, an RX self-capacitance detection channel 224 in the charge-to-code converter 216 measures an RX signal for each sensor electrode during a detection phase, according to the multi-phase detection process. In one embodiment, an RX signal is measured for each excitation signal pattern. The measured RX signals form a sequence of RX signals that is correlated with a predefined function, such as a sine function, to determine whether a hover input is present.

[0044] Fig. 5A and Fig. Figure 5B illustrates RX signal sequences resulting from hover inputs near different locations on a sensor array 103, according to one embodiment. As shown in Fig. As illustrated in Figure 5A, the sensor array 103 has 47 sensor electrodes. Therefore, a circularly rotating sequence of excitation signal patterns comprises 47 different patterns, which are applied to the sensor electrodes in sequence. Fig. 5B represents the vertical axis as a signal level of the RX signal as detected by the RX detection channel 224, and the horizontal axis represents the pattern number of the excitation signal pattern that induced the RX signal.

[0045] Fig. Figure 5B illustrates the RX signal sequences 511, 512, and 513, which result from the respective hover inputs 501, 502, and 503 at different locations on the sensor array 103. Hover input 501 is located at an edge of the sensor array 103, while hover inputs 502 and 503 are progressively closer to the center of the array 103. Each of the measured RX signal sequences 511–513 resembles a sine function; therefore, a sine function is used to approximate each of the RX signal sequences 511–513 by a least-squares approximation.

[0046] A sine function is generally expressed by parameters a, b, c and d in equation 5: y=a+b⋅sin(c⋅i+d)

[0047] In equation 5, y is the magnitude of the sine function corresponding to the magnitude of the RX signal, and i is the sensor electrode index. The parameter d reflects the phase shift of the sine function and is directly related to the hover position on the sensor array 103. Fig. 6A, Fig. 6B and Fig. Figure 6C illustrates approximations of the RX signal sequences 513, 512 and 511 by sine functions having different parameters, according to one embodiment. Fig. In 6A, the RX signal sequence 513 is approximated by a sine function 601 (illustrated as a continuous line). Fig. In 6B, the RX signal sequence 512 is approximated by a sine function 602. Fig. In 6C, the RX signal sequence 511 is approximated by a sine function 603. Each of the approximating sine functions 601-603 is defined using a different set of parameters a, b, c, and d and approximates the RX signal sequence with a high correlation coefficient.

[0048] Therefore, the parameters a, b, c, and d are calculated to determine a sine function that best approximates a measured RX signal sequence. One method for calculating the parameters c and d is based on minimizing equation 6 below using the least-squares method: Q=∑i=0N−1[a+b⋅sin(c⋅i+d)−Si]2

[0049] However, c can be more easily determined using trigonometric theory as follows by equation 7: c=2⋅πN−1

[0050] In equation 7, N represents the number of sensor electrodes and 2×π is the period of the sine function. In one embodiment, the parameter d defines an amount by which the sine function is shifted along the x-axis, as shown in Fig. 7 illustrated. In Fig. In equation 7, the sine function 701 has a value d that is equal to zero. The sine function 702 has a non-zero value d; accordingly, features of the sine function 702 are shifted by the amount d relative to the original sine function 701. The parameter d is calculated according to equation 8: d=−2⋅π⋅(idxMax−N−14)N−1

[0051] In equation 8, idxMax represents the index of the sensor electrode where the signal with the highest magnitude is measured, and N represents the number of sensor electrodes. The parameter d represents the displacement of the hover input (i.e., the position of the object relative to the sensor electrodes in array 103).

[0052] Based on equations 7 and 8, the argument becomes arg i calculated using the sine function as shown in equation 9: argi=c⋅i+d=2⋅πN−1⋅i−2⋅π⋅(idxMax−N−14)N−1or:argi=π⋅[4⋅(i−idxMax)+N−1]2⋅(N−1)

[0053] Equation 10 below follows from equations 5 and 9: y=a+b⋅sin(argi)=a+b⋅sin(π⋅[4⋅(i−idxMax)+N−1]2⋅(N−1))

[0054] The parameters a and b are calculated by minimizing Q in equation 11 as follows: Q=∑i=0N−1[a+b⋅sin(argi)−Si]2=>min

[0055] In equation 11, S i The measured response signal caused by the hover input at the sensor electrode with index i. Partial differentiation yields the equations below (12): {∂Q∂a=2⋅∑i=0N−1[a+b⋅sin(argi)−Si]=0∂Q∂b=2⋅∑i=0N−1[a+b⋅sin(argi)−Si]⋅sin(argi)=0

[0056] Equations 12 can be expressed as shown in Equations 13: {N⋅a+b⋅∑i=0N−1sin(argi)=∑i=0N−1Sia⋅∑i=0N−1sin(argi)+b⋅∑i=0N−1[sin(argi)]2=∑i=0N−1Si⋅sin(argi)

[0057] Equations 13 can be simplified by denoting variables sumSin, sumS, sumSinSin and sumSsin as shown below in Equations 14: sumSin=∑i=0N−1sin(argi)sumS=∑i=0N−1SisumSinSin=∑i=0N−1[sin(argi)]2sumSsin=∑i=0N−1Si⋅sin(argi)

[0058] Equations 13 can therefore be expressed as shown in Equations 15: {N⋅a+b⋅sumSin=sumSa⋅sumSin+b⋅sumSinSin=sumSsin

[0059] Solving the first equation of equations 15 for parameter a yields equation 16: a=sumS−b⋅sumSinN

[0060] Substituting equation 16 into the second equation of equations 15 yields equation 17 below: sumS−b⋅sumSinN⋅sumSin+b⋅sumSinSin=sumSsin

[0061] Solving equation 17 for parameter b yields equation 18: b=N⋅sumSsin−sumS⋅sumSinN⋅sumSinSin−sumSin2

[0062] In one embodiment, the equations above can be used to calculate the parameters a, b, c, and d of a sine function that approximates the measured RX signal sequence. It should be noted that the correlation coefficient between the RX signal sequence and the approximating sine function can be calculated without calculating the parameter a.

[0063] The sine function is calculated in post-processing block 222. Different approaches can be used to calculate the sine function in devices with limited computing resources. For example, the sine function can be calculated using a CORDIC calculation (CORDIC = Coordinate Rotation Digital Computer) or, alternatively, the Taylor series for the sine function, as expressed in Equation 19 below, where arg is the input argument of the sine function: sin(arg)=arg−arg33!+arg55!+arg77!+⋯+(−1)n−1(2n−1)!arg2n−1+⋯

[0064] In one embodiment, the sine function is stored tabularly in a lookup table (LUT) 225, which stores a result of the sine function for each of a range of possible input arguments. Therefore, the calculation of the correlation coefficient in the post-processing block 222 includes looking up the result in the LUT 225. Looking up the result in a table is not an iterative calculation and can therefore be performed with limited computation time and resources. Furthermore, tabular storage does not require a large amount of memory. For a sine function, data stored in the LUT 225 for the first quarter of the period can be used to construct the remainder of the period. In one embodiment, the LUT 225 occupies no more than 65 bytes of memory.

[0065] Fig. Figure 8 illustrates the subdivision of one period of a sine function into four quarters to reduce the size of the LUT 225, according to one embodiment. Fig. In section 8, the sine function y = sin(2πx / 256) has a period T equal to 256. Input arguments x falling within the first quarter (1001) are looked up directly in LUT 225 to determine the result of the sine function for the argument x. For input arguments x in the second quarter (1002), x is subtracted from 128, and the result is looked up in LUT 225. For input arguments x in the third quarter (1003), 128 is subtracted from x, and the result is looked up in LUT 225 and negated. For input arguments x in the fourth quarter (1004), x is subtracted from 256, and the result is looked up in LUT 225 and negated.

[0066] The input argument x is also limited to the range between 0 and 256, since the period of the sine function T = 256. In one embodiment, if x is greater than 256, a modulo operation (e.g., x mod 256) is performed to compute an input argument within the acceptable lookup range.

[0067] The input argument arg i The electrode index i is expressed in equation 9 above by i and idxMax. Equations 20 show the transformation of equation 9 from a sine function with period 2π to a sine function with period T = 256 by multiplying by the factor 256 / 2π. argi=256⋅π⋅[4⋅(i−idxMax)+N−1]2⋅π⋅2⋅(N−1)or:argi=256⋅(i−idxMax)N−1+64

[0068] After performing the period correction as shown in Equations 20, the resulting input argument can be arg i can be looked up in LUT 225.

[0069] Post-processing block 222 performs the sine function calculation when it calculates the correlation coefficient between the measured sequence of RX signals and its approximating sine function, as defined by parameters a, b, c, and d. To calculate the correlation coefficient, the means avSigOriginal and avSigAppr for the measured RX signal sequence and the approximating sine function are calculated as shown in equations 21 and 22 below. avSigOriginal=∑i=0N−1SiN avSigAppr=∑i=0N−1[a+b⋅sin(argi)]N=a+b⋅∑i=0N−1sin(argi)N

[0070] Equation 23 applies to the entire period of the sine function. ∑i=0N−1sin(argi)=0

[0071] Therefore, equation 22 can be reduced to show that avSigAppr equals a.

[0072] The square of the correlation coefficient is calculated as shown in equation 24. corrCoeff=256⋅(∑i=0N−1(Si−avSigOriginal)⋅(Sappri−avSigAppr))2∑i=0N−1(Si−avSigOriginal)2⋅∑i=0N−1(Sappri−avSigAppr)2

[0073] Equation 24 is represented by the designation deltaAppr i and deltaOriginal i as shown in equation 25, expressed more simply: {deltaAppri=Sappri−avSigAppr=a+b⋅sin(argi)−a=b⋅sin(argi)deltaOriginali=Si−avSigOriginal

[0074] As shown in Equation 25, the correlation coefficient can be calculated without calculating the parameter a. Combining Equation 24 with Equation 25 yields an expression for calculating the correlation coefficient, as shown in Equation 26. corrCoeff=256⋅(∑i=0N−1deltaOriginali⋅deltaAppri)2∑i=0N−1deltaOriginali2⋅∑i=0N−1deltaAppri2

[0075] The approximating sine curve is fitted by moving the coordinate of the largest signal to the left and to the right of the index of the sensor with the largest signal (i.e. idxMax) until the correlation coefficient value corrCoeff, as calculated according to Equation 26, is maximized.

[0076] In one embodiment, the post-processing block 222 detects a hover input based on the presence of an object near the sensor electrodes by comparing the maximized correlation coefficient with a predetermined correlation threshold. If the correlation coefficient exceeds the correlation threshold, then the measured RX signal sequence is sufficiently close to the approximating sine function, and the post-processing block 222 detects a hover input. The correlation threshold is a tunable parameter in the range between 0 and 255. In one embodiment, the correlation threshold is 75, so a hover input is detected when the correlation coefficient is greater than or equal to 75.

[0077] In one embodiment, a TX excitation signal pattern that does not have a zero sum can be analyzed similarly using a least-squares approximation instead of deconvolution. In one embodiment, a parabola is used to approximate the measured sequence of RX signals. According to this approach, a vertex for the approximating parabolic function is calculated based on a subset of the measured RX signals surrounding a maximum RX signal value. Fig. Figure 9 illustrates a section of an RX signal sequence 900, in which the maximum signal 901 has a value S i exhibits, according to one embodiment. The subset of measured RX signals surrounding the maximum 901 includes the signals S i-3 to S i+3 With reference to equation 27 below, the approximating parabolic function is defined by a quadratic curve, where Sappr i-jrepresents the value of the parabolic function that corresponds to a sensor electrode located j electrodes away from electrode i, where the maximum signal was measured. Sapphire−j=a+b⋅j+c⋅j2

[0078] The extremum of the parabola is calculated by setting the derivative of the quadratic function equal to 0, as shown in equations 28 below. y=a+b⋅x0+c⋅x02dydx0=b+2⋅c⋅x0=0x0=−b2⋅c

[0079] Equation 29 shows the function that is to be minimized according to the least squares approximation. Q=∑j=−33[a+b⋅j+c⋅j2−Si−j]2=>min

[0080] Partial differentiation of equation 29 yields equations 30. {∂Q∂a=2⋅∑j=−33[a+b⋅j+c⋅j2−Si−j]=0∂Q∂b=2⋅∑j=−33[a+b⋅ j+c⋅j2−Si−j]⋅j=0∂Q∂c=2⋅∑j=−33[a+b⋅j+c⋅j2−Si−j]⋅j2=0

[0081] Combining equations 28 and 30 yields equation 31 below. x0=−3⋅∑j=−33Si−j⋅j2⋅(∑j=−33Si−j⋅j2−4⋅∑j=−33Si−j)

[0082] Fig. Figure 9B illustrates a graph of the resulting approximation curve 911 along with the measured RX signal 910. The vertical axis represents the magnitude of the RX signal 910 and the approximation curve 911, while the lower axis of the graph represents the indices of the corresponding sensor electrodes. As illustrated, seven electrodes are used around the highest measured value; however, greater immunity to noise when detecting hover inputs is achieved by including a larger number of sensor electrode measurements in the detection process.

[0083] In one embodiment, a reciprocal quadratic curve is used as a predefined function to approximate a measured sequence of RX signals. The approximation using the reciprocal quadratic curve utilizes a larger number of available RX measurements, which increases the immunity to noise in the hover detection result, since noise-induced errors are averaged over a larger number of sensor electrodes. The reciprocal quadratic curve is defined in Equation 32 below. Sapphire = 1a + b⋅i + c⋅i²

[0084] In equation 32, Sappr iThe magnitude of the function result is , and i is the index of the sensor electrode for which the function result is calculated. The parameters a, b, and c correspond to a version of the curve that best approximates the measured sequence of RX signals. A correlation coefficient between the RX signal sequence and its approximating reciprocal quadratic curve is calculated according to Equation 38, derived as shown in Equations 33-38 below. For the reciprocal quadratic curve, a vertex x0 is equal to -b / 2c. Accordingly, Equation 32 can be expressed in terms of the vertex x0, as shown in Equations 33. Sappri=1a+b⋅i+c⋅i2Sappri=1a+c⋅i⋅(i−2⋅x0)

[0085] In equation 33, Sappr i The magnitude of the approximating function corresponds to the sensor electrode index i. The magnitude of the signal at the vertex x0 is assigned to a variable S in equations 34. p set. Sp=1a+c⋅p⋅(p−2⋅x0)a=1Sp−c⋅p⋅(p−2⋅x0)

[0086] In equation 34, S p the value of the peak size, while p The index corresponds to the electrode where the tip size is located. The function to be minimized according to the least-squares approximation process is expressed below in equation 35. Q=∑i=0N−1[c⋅(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))+1Sp−1Si]2=>min

[0087] Partial differentiation of equation 35 yields the equations 36 below. ∂Q∂c=2⋅∑i=0X−1[c⋅(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))+1Sp−1Si]⋅(i⋅(i−2⋅x0)−p⋅(p−2⋅x0)) =0c=∑i=0N−1(Sp−Si)⋅(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))SiSp⋅∑i=0N−1(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))2

[0088] Equations 36 are simplified by denoting variables A and B as shown below in Equations 37. {A=∑i=0N−1(Sp−Si)⋅(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))SiB=∑i=0N−1(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))2

[0089] Equations 38 result from substituting A and B into equations 36. Sappri=1a−2⋅c⋅x0⋅i+c⋅i2=Sp⋅BB+A⋅(i⋅(i−2⋅x0)−p⋅(p−2⋅x0))

[0090] The RX signals are normalized and the square of the correlation coefficient is calculated between the normalized RX signals and their approximation by the reciprocal quadratic curve according to equations 39 below. avSigNorm=∑i=0N−1SnormiNavSigAppr=∑i=0N−1SappriNcorrCoeff=256⋅(∑i=0N−1(Snormi−avS igNorm)⋅(Sappri−avSigAppr))2∑i=0N−1(Snormi−avSigNorm)2⋅∑i=0N−1(Sappri−avSigAppr)2

[0091] A hover input is detected when the correlation coefficient, corrCoeff, is greater than a correlation threshold. The correlation threshold is a tunable parameter between 0 and 255. In one embodiment, the correlation threshold has a value of 125.

[0092] Fig. Figure 10A illustrates, according to one embodiment, a reciprocal quadratic curve 1003 that approximates a sequence of RX signals 1001 resulting from a hover input at sensor electrode 18. The raw RX signal sequence 1001, as measured at sensor electrodes 0-44, is normalized to generate a normalized sequence of RX signals 1002. The normalized signal sequence 1002 is approximated by the curve 1003. The vertex of the curve 1003 corresponds to the location of the hover input at sensor electrode 18. Fig. For 10A, the correlation coefficient is 246; therefore, the presence of the hover input is detected because the correlation coefficient is greater than the correlation threshold of 175.

[0093] Fig. Figure 10B similarly illustrates, according to one embodiment, a reciprocal quadratic curve 1013 that approximates a sequence of RX signals 1011 resulting from a hover input at sensor electrode 12. The raw RX signal sequence 1011, as measured at sensor electrodes 0-44, is normalized to generate a normalized sequence of RX signals 1012. The normalized signal sequence 1012 is approximated by the curve 1013. The vertex of the curve 1013 corresponds to the location of the hover input at sensor electrode 12. Fig. For 10B, the correlation coefficient is 234; therefore, the presence of the hover input is detected because the correlation coefficient is greater than the correlation threshold of 175.

[0094] Fig. Figure 11 illustrates a reciprocal quadratic curve 1103 that approximates a sequence of RX signals 1101 resulting from heating of the sensor array rather than from a hover input. The raw RX signal sequence 1101, as measured at sensor electrodes 0-44, is normalized to produce a normalized sequence of RX signals 1102. The normalized signal sequence 1102 is approximated by the curve 1103. The vertex of the curve 1103 corresponds to the location of the highest signal magnitude at sensor electrode 14. Fig. 11. The correlation coefficient is 60; therefore, the presence of the hover input is not detected because the correlation coefficient is less than the correlation threshold of 175.

[0095] In one embodiment, a subset of nine signals surrounding the local maximum signal in the RX signal sequence is approximated by a cubic curve to determine the position of the hover input. According to this approach, values ​​of the differences Diff i-1 , Diff i , Dif i+1 and diff i+2 the RX signal sequence (each corresponding to sensor electrodes i-1, i, i+1 and i+2) in linear proportion to the indices of their corresponding sensor electrodes. Fig. Figure 12 illustrates this linear relationship by graphically representing the difference values ​​Dif i-1 , Dif i , Dif i+1 and differential i+2 relative to the sensor electrode indices, according to one embodiment. The upper section of the Fig. 12 shows the signal quantities S i-4 to S i+4 for the sensor electrodes. The intersection of this line with the x-axis indicates the hover position.

[0096] The intersection point x is calculated according to equation 40, where N is the number of sensor electrodes and ResX is the resolution of the sensor array along the (horizontal) X-axis in pixels. x=Re sXNx⋅(i−Diffi−1Diffi−Diffi−1)

[0097] The coefficients of the approximating cubic curve are determined according to equation 41. Si−j=a−b⋅j−c⋅j2

[0098] Accordingly, the function to be minimized for the least squares approximation is shown in equation 42. Q=∑j=−44[Si−j−a−b⋅j−c⋅j2]2=>min

[0099] Partial differentiation of equation 42 yields equations 43. {∂Q∂a=−2⋅∑j=−44[Si−j−a−b⋅j−c⋅j2]=0∂Q∂b=−2⋅∑j=−44[Si− j−a−b⋅j−c⋅j2]⋅j=0∂Q∂c=−2⋅∑j=−44[Si−j−a−b⋅j−c⋅j2]⋅j2=0

[0100] Equation 40 can therefore be expressed as shown in Equation 44. x=Re sX256⋅Nx⋅(256⋅i+128−9856⋅∑j=−44Si−j⋅j5⋅(3⋅∑j=−44Si−j⋅j2−20⋅∑j=−44Si−j))

[0101] The value calculated for x according to the equations above provides a coordinate for the detected hover input in the form of the sensor electrode indices.

[0102] In one embodiment, immunity to noise from nearby sources (e.g., LCD common-mode noise) is improved by reducing the number of sensor electrodes at which RX signals are measured. This is because the charge introduced into the acquisition channel by the LCD panel is proportional to the number of electrodes simultaneously acquired by the channel, or, in other words, proportional to the coupling capacitance between the respective RX sensor electrode and the LCD panel. Fig. Figures 13A-13C illustrate sequences of excitation signal patterns according to an embodiment, wherein TX excitation signals are applied to the sensor electrodes in a non-circular manner and the RX signal for each pattern is measured at a single sensor electrode via a single RX detection channel.

[0103] With reference to Fig. 13A applies a positive-phase excitation signal to each of the excitation signal patterns 1320 (comprising signal patterns 1320(1)-1320(10)) in sequence 1300 to sensor electrodes that surround and encompass the sensor electrode at which the RX signal is measured (shown in thick outline). For example, in pattern 1320(5), the positive-phase excitation signals are applied to electrodes 3-7. Each of the signal patterns in sequence 1300 is a zero-sum pattern; therefore, the complementary negative-phase excitation signals are applied to the remaining electrodes 1-2 and 8-10 in signal pattern 1320(5). A reduction in the magnitude of the measured RX signal due to fewer measured sensor electrodes is compensated for by an associated reduction in external noise (e.g., from an LCD display).

[0104] Fig. Figure 13B illustrates a sequence 1301 of signal patterns 1330, comprising patterns 1330(1)-1330(10). In each of the excitation signal patterns 1330, no excitation signal is applied to some of the sensor electrodes (indicated by '0'), including the sensor electrode at which the RX signal is measured. In each pattern, the positive-phase excitation signals are applied to the sensor electrodes surrounding the detected electrode. Each of the patterns 1330 is also a zero-sum signal pattern.

[0105] Fig. Figure 13C illustrates a signal pattern sequence 1302 in which, for each of the excitation signal patterns 1340, no excitation signal is applied to a larger number of sensor electrodes, including the sensor electrode at which the RX signal is measured. Each of the patterns 1340 is also a zero-sum signal pattern.

[0106] Fig. Figures 14A-14C illustrate an approach to increase the measurable RX signal during the detection of a hover input according to one embodiment. Fig. Figure 14A illustrates a sequence of excitation patterns 1402 for detecting a hover input 1401 at the sensor array 103 as previously described, wherein the column sensor electrodes are excited by a sequence of column excitation patterns 1403, which are circularly rotating zero-sum patterns. For each of the column patterns 1403, no excitation signal is applied to the row sensor electrodes. Accordingly, the row excitation patterns 1404 are zero for each of the sensor patterns 1402.

[0107] If the position of the hover input 1401 along the vertical axis is known (e.g., if the line sensor electrodes have been previously detected), the measurable RX signal can be increased by applying positive phase excitation signals to a subset of the line sensor electrodes that are closest to the hover input 1401, while applying negative phase excitation signals to the remaining line sensor electrodes. Fig. Figure 14B illustrates this approach according to one embodiment. A sequence of six sensor patterns 1412 is applied to the sensor array 103. For each of the column excitation signal patterns 1403, one of the row excitation signal patterns 1414 is applied to the row sensor electrodes based on a previously determined position of the hover input 1401. Each of the row excitation signal patterns 1414 is a zero-sum pattern and applies a positive-phase excitation signal to the four row sensor electrodes closest to the hover input 1401, while a complementary negative-phase excitation signal is applied to the remaining four row sensor electrodes.

[0108] This measurement procedure raises the baseline of the measured RX signals but does not increase the dynamic range of the RX signals. In one embodiment, the magnitude of the RX signal measured for each of the sensor patterns 1412 is increased by approximately 175 fF, while the dynamic range of the RX signals (i.e., the difference between the lowest and highest RX signal) remains the same (approximately 150 fF). A similar effect occurs in the opposite direction (i.e., the baseline is reduced) when the phase polarity of the TX excitation signals is reversed (i.e., positive-phase excitation signals are swapped with negative-phase excitation signals).

[0109] Fig. Figure 14C illustrates an embodiment in which the positive and negative phase signals alternate in the sequence of signal patterns 1424 applied to the line sensor electrodes. The phase polarities alternate between successive patterns in the sequence 1412. In alternative embodiments, the phase polarities alternate after every second pattern, third pattern, and so on. Alternating the phase polarities for signal patterns applied to the line sensor electrodes increases the dynamic range of the measured RX signal sequence without increasing the baseline. In one embodiment, the dynamic range is increased to approximately 500 fF.

[0110] Fig. Figure 15 illustrates a graph with RX signal sequences for the different measurement approaches according to one embodiment. RX signal sequence 1501 results from measuring a hover input 1401 when no excitation signal is applied to the line sensor electrodes. RX signal sequences 1502 and 1503 represent the cases in which positive-phase and negative-phase excitation signals, respectively, are applied to the line sensor electrodes closest to the hover input 1401. RX signal sequence 1504 is generated when alternating positive- and negative-phase excitation signals are applied to the line sensor electrodes closest to the hover input 1401, with the alternating pattern occurring every second time. This type of drive method can be described as "correlated double sampling" and reduces drift in the receive channels.

[0111] Fig. Figure 16 is a flowchart illustrating a process 1600 for detecting a hover input on a capacitive sensor array 103 according to one embodiment. The process 1600 is performed by components in the processing device 102.

[0112] In block 1601, the sequencer 223 determines an initial TX signal pattern from a sequence of TX signal patterns for application to the sensor array 103. In block 1603, the TX generator 215 applies the initial TX signal pattern to the set of TX electrodes in the sensor array 103 by applying, for each sensor electrode in a set of sensor electrodes (e.g., the column sensor electrodes in the sensor array 103), either a positive-phase excitation signal or a negative-phase excitation signal according to the TX signal pattern. The negative-phase excitation signal is complementary to the positive-phase excitation signal, and the positive- and negative-phase excitation signals are applied to sensor electrodes that are contiguous in a circular sequence of sensor electrodes.

[0113] In one embodiment using zero-sum excitation patterns, the positive-phase excitation signal is applied to one half of the sensor electrodes in the set, and the negative-phase excitation signal is applied to the remaining half. Accordingly, the magnitude of the sum of the excitation signals applied to the sensor electrodes is reduced to less than the magnitude of any single excitation signal applied to an individual sensor electrode, ideally approaching zero. In alternative embodiments using a non-zero-sum excitation pattern, the excitation of the sensor electrodes produces at least as much emission as a single sensor electrode to which an excitation signal is applied. In Block 1605, the resulting RX signal induced by the application of the initial TX signal pattern is measured by the RX channel 224 in the charge-to-code converter 216.

[0114] If there are still TX signal patterns in the sequence in block 1607 that have not yet been applied to the sensor electrodes, then process 1600 returns to block 1601. In block 1601, the sequencer circuit 223 determines the next TX signal pattern in the sequence of TX signal patterns based on a circular rotation of the initial TX signal pattern. In one embodiment, the current pattern of positive and negative phase excitation signals is circularly shifted around a sensor electrode to generate the next TX signal pattern in the sequence. Therefore, for each TX signal pattern, the excitation signal applied to each sensor electrode corresponds to the excitation signal applied during the application of the preceding TX excitation signal pattern to a preceding sensor electrode (i.e., the previous one).(with a lower index) in a circular sequence of the set of sensor electrodes, wherein the preceding TX excitation signal pattern is the pattern in the sequence that was last applied before the current signal pattern. In alternative embodiments, the current pattern is shifted by more than one electrode.

[0115] Blocks 1601-1607 are repeated in a loop, so that each of the TX signal patterns in the sequence is applied sequentially to the set of sensor electrodes, and an RX signal response is measured for each TX signal pattern. The measured sequence of RX signals is stored in memory as a vector 211. In block 1607, once all the TX signal patterns have been applied, the post-processing block 222 calculates the correlation coefficient between the measured sequence of RX signals and a least-squares approximation of the RX signal sequence by a predefined function, such as a sine function, as provided in blocks 1609-1603.

[0116] In block 1609, some parameters of the approximating sine function are calculated; for example, parameters c and d are calculated using equations 7 and 8. Parameter d represents a displacement indicating the position of the hovering object relative to the sensor electrodes and is calculated based on the largest signal in the measured sequence of RX signals.

[0117] In block 1611, post-processing block 222 determines the remaining parameters for the sine function using a least-squares calculation to approximate the sequence of RX signals. In one embodiment, LUT 225 stores a result of the sine function for each of a number of possible input arguments. Post-processing block 222 accesses LUT 225 when calculating the remaining parameters (e.g., parameter b according to equation 18).

[0118] In block 1613, post-processing block 222 calculates the correlation coefficient between the measured RX signal sequence and its approximation by the sine function, which has the calculated parameters. In one embodiment, the approximating sine curve is adjusted by shifting the sine curve relative to the sensor electrode indices until the correlation coefficient, as calculated according to equation 26, is maximized.

[0119] In block 1615, post-processing block 222 compares the correlation coefficient to a predefined correlation threshold. If the correlation coefficient in block 1617 does not exceed the correlation threshold, then no hover input is detected, and post-processing block 222 reports in block 1621 that no hover input is present. From block 1621, process 1600 returns to block 1601 to restart the detection process. In block 1617, if the correlation coefficient exceeds the correlation threshold, process 1600 proceeds to block 1619. In block 1619, the presence of a valid hover input is detected. From block 1619, the presence of the detected hover input and its location (as indicated by the displacement parameter d) are transmitted to the host device 101, which performs some action (e.g., updating a cursor on the display 104, highlighting or selecting a menu item, etc.).Process 1600 returns to block 1601 to resume applying the TX excitation signal patterns to the sensor electrodes for the next measurement cycle, again starting with the initial TX excitation signal pattern in the sequence. Process 1600 therefore continuously repeats blocks 1601-1619 to detect the presence of hover inputs and their positions over time.

[0120] Fig.Figure 17 is a flowchart illustrating, according to one embodiment, a process 1700 for detecting a hover input based on a measured sequence of RX signals, which uses a non-zero-sum scan sequence and a deconvolution procedure. In process 1700, a parabola or a reciprocal quadratic function is used for the least-squares approximation instead of a sine function. Process 1700 is performed by components in the processing device 102, which comprise the post-processing block 222. Starting from block 1607 in process 1600, process 1700 begins with block 1701. At this point, an RX signal has been measured for each of the TX excitation signal patterns in the sequence of TX excitation patterns.

[0121] In block 1701, post-processing block 222 determines the vertex of the approximating function (i.e., the parabola or the reciprocal quadratic curve). The vertex of the approximating function is equated with the measured RX signal that has the highest magnitude. From block 1701, process 1601 continues with block 1703.

[0122] In block 1703, post-processing block 222 normalizes the measured RX signal sequence and approximates a section of the normalized RX signal sequence (e.g., seven RX signals closest to the largest RX signal) using the parabola whose vertex is set as provided in block 1701. Alternatively, all of the available RX signals in the normalized sequence are approximated using the reciprocal curve whose vertex is set as provided in block 1701.

[0123] In block 1705, a correlation coefficient is calculated, indicating the degree of correlation between the sequence of normalized RX signals (scaled down to a global peak) and the approximating function of the simulated hover profile, which has its peak at a vertex calculated according to the parabolic function. In block 1707, the correlation coefficient is compared to a predefined correlation threshold. If the correlation coefficient in block 1709 does not exceed the threshold, then no hover input is detected. The absence of any hover input is reported in block 1717. Process 1700 then returns to block 1601 to resume applying the sequence of TX excitation patterns to the sensor electrodes.

[0124] If the correlation coefficient in block 1709 exceeds the specified correlation threshold, the post-processing block 222 detects a valid hover input, as provided in block 1711. In one embodiment, the location of the hover input relative to the sensor electrodes is determined according to blocks 1713 and 1715. In block 1713, a set of differentials is calculated for a subset of the RX signals that are closest to the highest-magnification RX signal. In block 1715, the hover position is calculated from the intersection of the x-axis with a regression line calculated for the differentials. From block 1715, the presence of the hover input and its location are communicated to the host device 101, which performs some action based on the presence of the hover input and / or its location.Process 1700 returns to block 1601 and resumes applying TX signal patterns to the sensor electrodes for the next measurement cycle.

[0125] Various modifications can be made to the preceding embodiments; for example, signals described as being activated at a high voltage can instead be activated at a low voltage, or specified components can be replaced by other components that have similar functionality. As described herein, conductive electrodes that are "electrically connected" or "electrically coupled" can be coupled such that a conductive path with a relatively low resistance exists between the conductive electrodes.Quantities, measures, or other values ​​described as “essentially” the same may be nominally equal but need not be exactly the same (with variations due to manufacturing tolerances, environmental conditions, quantization or rounding errors, and / or other factors), or may be sufficiently close to the same to achieve an intended effect or benefit.

[0126] The embodiments described herein include various operations. These operations may be performed by hardware components, software, firmware, or a combination thereof. As used herein, the term "coupled with" may mean direct or indirect coupling through one or more intermediary components. Any signals provided via different buses described herein may be time-division multiplexed with other signals and provided via one or more common buses. Additionally, the interconnection between circuit components or blocks may be shown as buses or as individual signal lines. Each of the buses may alternatively be one or more individual signal lines, and each of the individual signal lines may alternatively be a bus.

[0127] Certain embodiments may be implemented as a computer program product, which may include instructions stored on a computer-readable medium. These instructions can be used to program a general-purpose or specialized processor to perform the described operations. A computer-readable medium includes any mechanism for storing or transmitting information in a form (e.g., software, processing application) that can be read by a machine (e.g., a computer). The computer-readable storage medium may, but is not limited to, include: a magnetic storage medium (e.g., a floppy disk); an optical storage medium (e.g., a CD-ROM); a magneto-optical storage medium; a fixed-order memory (ROM); a working memory (RAM); a erasable programmable memory (e.g., a programmable memory).EPROM and EEPROM); a flash memory or other type of medium suitable for storing electronic instructions.

[0128] Additionally, some implementations can be carried out in distributed computing environments where the computer-readable medium is stored on and / or executed by more than one computer system. Furthermore, the information transmitted between computer systems can be transferred either by pull or push over the transmission medium connecting the computer systems.

[0129] Although the operations of the method(s) herein are shown and described in a specific order, the order of operations of each method may be modified so that certain operations may be performed in reverse order or so that a certain operation may be performed at least partially concurrently with other operations. In another embodiment, instructions or sub-operations of individual operations may take place in an intermittent and / or alternating manner.

[0130] In the preceding patent description, the claimed subject matter was described with reference to specific embodiments thereof. However, it will be obvious that various modifications and changes can be made to it without deviating from the broader scope of the invention as set out in the appended claims. Accordingly, the patent description and the drawings are to be regarded in an illustrative rather than a limiting sense.

Claims

[1] A capacity measurement device (100) comprising the following: a transmit (TX) generator (215) configured to generate a sequence of receive (RX) signals by applying each TX signal pattern (420) in a sequence of TX signal patterns (400) to a set of sensor electrodes (1-24), wherein the TX generator (215) is configured for each TX signal pattern (420) in the sequence of TX signal patterns (400), to apply a first excitation signal and a second excitation signal to each subset of a plurality of subsets, each comprising three or more connected sensor electrodes of the set of sensor electrodes (1-24), wherein the plurality of subsets includes at least half of the sensor electrodes in the set of sensor electrodes (1-24); a sequencer circuit (223) coupled to the TX generator (215) and configured to determine, for each TX signal pattern (420) in the sequence of TX signal patterns (400), a next subsequent TX signal pattern in the sequence based on a circular rotation of the TX signal pattern; and a processing block (222) coupled to the TX generator (215) and configured to detect the presence of an object near the set of sensor electrodes (1-24) based on a correlation measure between the sequence of RX signals and a predefined function. [2] Capacitance detection device (100) according to claim 1, wherein for each TX signal pattern (420) in the sequence of TX signal patterns (400), for each sensor electrode in the set of sensor electrodes (1-24), an excitation signal applied to the sensor electrode corresponds to an excitation signal that was applied during the application of a preceding TX signal pattern in the sequence of TX signal patterns (400) to a preceding sensor electrode in a circular sequence of the set of sensor electrodes (1-24). [3] Capacity detection device (100) according to claim 1, wherein the second excitation signal is complementary to the first excitation signal; and a magnitude of a sum of excitation signals applied to the set of sensor electrodes (1-24) is smaller than the magnitude of any one of the excitation signals applied to the set of sensor electrodes (1-24), where the excitation signals include the first excitation signal and the second excitation signal. [4] Capacitance sensing device (100) according to claim 1, wherein the TX generator (215) is further configured for each TX signal pattern (420) in the sequence of TX signal patterns (400) to: apply the first excitation signal to connected sensor electrodes that are connected in a circular sequence of the set of sensor electrodes (1-24), wherein the connected sensor electrodes comprise at least half of the set of sensor electrodes (1-24). [5] Capacity measurement device (100) according to claim 1, wherein: the given function is a sine function; and the processing block (222) is further configured to calculate the correlation measure by calculating parameters for the sine function based on a least-squares approximation of the sequence of RX signals (511-513). [6] Capacitance measurement device (100) according to claim 1, wherein each RX signal in the sequence of RX signals (511-513) represents an intrinsic capacitance which is measured at the set of sensor electrodes (1-24). [7] Capacity measurement device (100) according to claim 1, wherein: the processing block (222) is further configured to calculate a displacement based on a least-squares approximation of the sequence of RX signals (511-513); and The displacement indicates a position of the object relative to the set of sensor electrodes (1-24). [8] Capacity measurement device (100) according to claim 1, wherein: The correlation measure includes a correlation coefficient; and The processing block (222) is further configured for the following: Comparing the correlation coefficient with a given threshold; and Detecting the presence of the object when the correlation coefficient exceeds the specified threshold. [9] Capacity measurement device (100) according to claim 1, further comprising: an RX acquisition channel (224) coupled to the processing block (222) and configured to measure for each sensor electrode in the set of sensor electrodes (1-24) an RX signal of the sequence of RX signals after one of the first excitation signal and the second excitation signal has been applied to the sensor electrode. [10] A procedure that includes the following: Generating a sequence of RX signals by applying each TX signal pattern (420) in a sequence of TX signal patterns (400) to a set of sensor electrodes (1-24), wherein the application of each TX signal pattern (420) to the set of sensor electrodes (1-24) for each subset of a plurality of subsets, each comprising three or more connected sensor electrodes of the set of sensor electrodes (1-24), includes the application of a first excitation signal and a second excitation signal to the subset, wherein the plurality of subsets comprises at least half of the sensor electrodes in the set of sensor electrodes (1-24); for each TX signal pattern (420) in the sequence of TX signal patterns (400), determining a next subsequent TX signal pattern in the sequence based on a circular rotation of the TX signal pattern; and Detecting the presence of an object near the set of sensor electrodes (1-24) based on a correlation measure between the sequence of RX signals and a predefined function. [11] Method according to claim 10, wherein the application of each TX signal pattern to the set of sensor electrodes (1-24) further comprises, for each sensor electrode in the set of sensor electrodes (1-24), the application of an excitation signal to the sensor electrode, which corresponds to an excitation signal that was applied during the application of a preceding TX signal pattern in the sequence of TX signal patterns (400) to a preceding sensor electrode in a circular sequence of the set of sensor electrodes (1-24), wherein the preceding TX signal pattern is a TX signal pattern that was last applied to the set of TX sensor electrodes before the application of the TX signal pattern. [12] Method according to claim 10, wherein the second excitation signal is complementary to the first excitation signal; and a magnitude of a sum of excitation signals applied to the set of sensor electrodes (1-24) is smaller than the magnitude of any one of the excitation signals applied to the set of sensor electrodes, wherein the excitation signals include the first excitation signal and the second excitation signal. [13] Method according to claim 10, further comprising for each TX signal pattern (420) in the sequence of TX signal patterns (400) the following: Applying the first excitation signal to connected sensor electrodes that are connected in a circular sequence of the set of sensor electrodes (1-24), wherein the connected sensor electrodes comprise at least half of the set of sensor electrodes (1-24). [14] The method according to claim 10, further comprising: Calculating the correlation measure by calculating parameters for the given function based on a least-squares approximation of the sequence of RX signals (511-513), where the given function is a sine function. [15] Method according to claim 10, wherein each RX signal in the sequence of RX signals (511-513) represents an intrinsic capacitance which is measured at the set of sensor electrodes (1-24). [16] The method according to claim 10, further comprising: Calculating a displacement based on a least-squares approximation of the sequence of RX signals (511-513), where the displacement indicates a position of the object relative to the set of sensor electrodes (1-24). [17] The method according to claim 10, further comprising: Comparing the correlation measure with a predetermined threshold; and Detecting the presence of the object when the correlation measure exceeds the specified threshold, where the correlation measure includes a correlation coefficient. [18] A capacity measurement system (100) comprising the following: a host device (101); a capacitive sensor array (103) comprising a set of sensor electrodes (1-24); a TX generator (215) configured to generate a sequence of RX signals by applying each TX signal pattern (420) in a sequence of TX signal patterns (400) to the set of sensor electrodes (1-24), wherein the TX generator (215) is configured for each TX signal pattern (420) in the sequence of TX signal patterns (400) to apply a first excitation signal and a second excitation signal to each subset of a plurality of subsets, each comprising three or more connected sensor electrodes of the set of sensor electrodes (1-24), wherein the plurality of subsets comprises at least half of the sensor electrodes in the set of sensor electrodes (1-24); a sequencer circuit (223) coupled to the TX generator (215) and configured to determine, for each TX signal pattern (420) in the sequence of TX signal patterns (400), a next subsequent TX signal pattern in the sequence based on a circular rotation of the TX signal pattern; and a processing block (222) coupled to the host device (101) and the TX generator (215) and configured to detect the presence of an object near the set of sensor electrodes (1-24) based on a correlation measure between the sequence of RX signals and a predefined function. [19] Capacity recording system (100) according to claim 18, wherein: the second excitation signal is complementary to the first excitation signal; and a magnitude of a sum of excitation signals applied to the set of sensor electrodes (1-24) is smaller than the magnitude of any one of the excitation signals applied to the set of sensor electrodes (1-24), where the excitation signals are the first excitation signal and include the second excitation signal; and The processing block (222) is further configured for the following: Calculating the correlation measure by calculating parameters for the sine function based on a least-squares approximation of the sequence of RX signals (511-513) and Detecting the presence of the object when the correlation measure exceeds a predefined threshold. [20] Capacitance measurement system (100) according to claim 18, wherein each RX signal in the sequence of RX signals (511-513) represents an intrinsic capacitance which is measured at the set of sensor electrodes (1-24). [21] Capacitive measurement system (100) according to claim 18, further comprising a display (104) coupled to the host device (101), wherein the capacitive sensor array (103) is located on the display (104) and wherein the host device (101) is configured to update the display (104) in response to the detected presence of the object. [22] Capacity measurement system (100) according to claim 18, wherein the processing block (222) is further configured to detect a hover input by the object based on the sequence of RX signals, and wherein the host device (101) is configured to execute a first subroutine in response to the hover input.

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