Low-Power Spike-Mode Silicon Neuron for Capacitive ... - IEEE Xplore

Low-Power Spike-Mode Silicon Neuron for Capacitive Sensing of a Biosensor Qingyun Ma, Mohammad Rafiqul Haider, Vinaya Lal Shrestha, and Yehia Massoud Department of Electrical and Computer Engineering The University of Alabama at Birmingham Birmingham, Alabama 35294-4551 Email: {maq, mrhaider, vinaya, massoud}@uab.edu

Abstract—Neuromorphic computation promises to be an energy-efficient information processing technique both for the biological and the real-world environments. In this paper a novel structure of silicon neuron has been designed for measuring the variation of a sensor capacitance. The current-reuse technique and the subthreshold region operation of MOSFETs help achieving ultra-low-power consumption. The proposed silicon neuron is designed and simulated in 0.13-µm standard CMOS technology. The entire unit consists of 43 transistors and consumes only 33 nW with a supply voltage of 1 V. The output frequency is proportional to the variation of the sensor capacitance.

I. I NTRODUCTION In recent years technological improvements of micro- and nano- technology have made it possible to fabricate numerous micro- or nano- devices for sensing applications. Microelectronic biosensors as a kind of capacitive sensors are being used in a variety of applications in the engineering, environmental, and medical fields [1],[2],[3]. Higher resolutions, lower cost and miniaturization have offered the capacitive sensors a wide variety of applications ranging from biomolecule detection to the stimulus sensing in a tactile sensor [4]. Miniaturized electronic biosensors also made it possible to monitor the real time responses of living cells without altering the biochemical composition of the environment [5]. Capacitance variation of a sensor can be easily transformed into a frequency modulated signal by the use of LC oscillator or ring oscillator structures [6]. However, the higher power consumption and excessive area requirements of on-chip inductors make the designs inconvenient for lowpower biosensing applications. In recent years several research groups are involved in improving the power efficiency and lowering the size of the electronic biosensors [5]. Ultra-lowpower computational techniques of biological elements such as neurons, have also motivated the researchers to come up with a bioinspired sensor read-out solution [7]. Neurons in living organisms use chemical mechanisms to generate electrical signals that act together to represent and respond to physical behaviors. A neuron network, such as a human brain, can handle a large quantity of physical events, and solve numerous difficult tasks. Electrical signals are produced by manipulating the neuron membrane conductance for various ions. In 1952, Hodgkin, Huxley and Katz unveiled the main properties of ionic conductance underlying the neuron action potential. The Hodgkin and Huxley neuron model,

which is based on the results of experiments performed on squid axon, represents the electrical properties of segments of nerve membrane with an equivalent circuit [8]. The Hodgkin-Huxley (H-H) model is the most accurate representation of the ionic dynamics [8]. Owing to the hardware complexities of the H-H model, researchers have investigated bioinspired silicon (Si) neuron architectures as well. However, most of them can only partially reproduce the dynamics of a real neuron. The Hodgkin-Huxley formalism is a neuromimetic design that relies on biophysically realistic parameters such as the ionic flow through the membrane. The H-H formalism manifests a set of equations and an electrical equivalent circuit. The ionic flow through the membrane creates change of action potentials which is recorded as spikes. Conductance based H-H type neuron model has been reported, but it takes a large number of transistors [9]. In this paper, a new capacitance biosensor, based on fourchannel bursting H-H neuron model, has been presented. The T-type calcium-channel reflects the relationship between the sensor capacitance and the variation of the output spiking signal frequency. The proposed structure highly improves the power efficiency by eliminating the duplicated current flow. The entire structure has been designed using 0.13-µm CMOS technology. The new schematic achieves ultra-lowpower consumption as a consequence of a design with a reduced number of transistors that operate in the subthreshold region. The output envelop frequency is proportional to the variation of the sensor capacitance. The simulation results indicate that the total power consumption is only 33 nW. Section II describes the proposed Si neuron architecture. Section III presents the simulation results. Concluding remarks are presented in section IV. II. P ROPOSED S I N EURON A RCHITECTURE A H-H model based Si neuron that mimics a real neuron’s internal behavior, has been implemented. Fig. 1 shows the functional block diagram of a single Si neuron model, which includes four ion channels. The first, second and third channels are defined as high-threshold-voltage sensitive L-type Ca current, low-threshold-voltage sensitive rapidly inactivating Ttype Ca current and voltage-sensitive K current, respectively. The fourth channel carries leakage current, which represents the rest of the ionic flows in the cell. While the K channel

Ca-L Block

Sigmoid

L. D. F

Ca-T Block

Sigmoid

L. D. F

Fig. 2.

Ionic currents for different channels with the membrane potential.

K Block

Sigmoid

increasing membrane potential. As a consequence of high membrane potential, the L-type calcium channel is turned on, and the L-type current starts to discharge the membrane capacitor. However, as the charging process is faster than the discharging process, the membrane voltage still increases. When the membrane potential finally reaches a certain value, the T-type calcium channel opens and completely discharges the membrane capacitor. This process is repeated.

L. D. F

L Block

A. L-type Calcium Channel

Fig. 1. Hodgkin-Huxley Bursting Neuron model. L.D.F stands for log-domain filter.

charges the membrane capacitance, the Ca channels (both the L-type and the T-type) discharge the membrane capacitance. The membrane voltage is a function of the sum of the ionic currents in the four channels that flow into the membrane capacitance Cmem . The voltage across the membrane capacitance is, Cmem

X dVmem = Ileak + Ij , dt j

(1)

where Cmem is the membrane capacitance, Vmem is the membrane potential, Ij is the current from the different ionic channel associated with the j th membrane’s voltagedependent-conductance. The H-H model contains two different states - ‘activation’ and ‘inactivation’ [8]. In this design only activation state is used to define the ionic currents. The currents due to voltagedependent conductance is, Ij = g¯j .m˜j .(Ej − V ),

(2)

where g¯j is the maximal conductance, m˜j is the normalized state variable, and Ej is the reversal potential. Fig. 2 depicts the variation of ionic currents with the variation in membrane potential. Initially, the membrane capacitor has no charge. The leakage current slowly charges this capacitor to the certain potential, which opens up the potassium channel. The potassium current increases with the

The L-type calcium current channel is shown in Fig. 3(a). The subthreshold MOSFET and current reuse techniques have been used in this design. Conventional parallel architecture does not provide enough power saving due to the requirements of too many bias currents. On the contrary, in this design functional blocks are stacking on top of each-other to reuse the current used by the previous block. The current from the sigmoid function block directly goes into the log-domain filter whereas the mirrored branch of the log-domain filter controls the bias current of the linear transconductor. For this channel, the sigmoid function is implemented using body controlled differential pair. The Vs Ca L port controls the middle position of the sigmoid function in L-type calcium channel block. The Vk Ca L port controls the final output current level. B. T-type Calcium Channel The T-type calcium current channel is shown in Fig. 3(b). The gate controlled sigmoid function has been used to accelerate the current change by the variation of the membrane voltage. The Vs Ca L port controls the output current rising point whereas the falling point is controlled by the Vk Ca T port. 1) Log-Domain Low Pass Filter: Using translinear concept the working principle of the log-domain filter can be explained. The circuit schematic of the log-domain filter is shown in Fig. 4. M1 and M2 form the current mirror and Md works as a diode resistor. The value of the diode resistor is optimized by computer simulation to maintain the mirror action. The input current Iin and the output current Iout are related as, dIout Iin − Iout = Csensor VT dt κI 1

(3)

M1

M3

M2

M1

M4

M3

I = 4 nA M5

M6

VMem

M2

M4 I = 2 nA

ICa_L_out

M1

M5

M3 M6

ICa_T_out

VMem

M9 M10

Vk_Ca_L

(b) Fig. 3.

M6

Vs_K

M9

M10

VMem

Vk_K M11

M8 C = 100pF

(c)

(a) L-type Ca channel (b) T-type Ca channel (c) Potassium (K) channel.

the differential coefficient. The schematic consist of one pair of current mirror and a linear transconductance. The final output current level controlled by the VKL port is directly fed into the membrane capacitor.

Iin Md M1

M2 Iout

dv dt

Csensor

Schematic of the log-domain filter.

2) Sensor capacitor: The sensor capacitor, Csensor is a part of the log-domain filter in the T-type calcium channel. The time constant of the log-domain filter is related to the variation of the sensor capacitance, τ=

M13

C = 100pF

(a)

Fig. 4.

M12

M7

C = 500 fF

IC = C

M15

M11

M8 M7

I1

M14

IK_out

M5

Vk_Ca_T

M7

 

I = 4 nA VMem

M9 M10

VMem

M11

M8

M4

Vs_Ca_T

VMem

Vs_Ca_L

M2

Csensor VT . κI1

(4)

The delay from the log-domain filter controls the output current flow of the T-type calcium channel. The delay variation of the output current, which is the key of the silicon neuron behavior, controls the frequency of the spiking signal. C. Potassium Channel The potassium channel as shown in Fig. 3(c) has a great similarity as the L-type calcium channel. The current reuse technique and the body controlled sigmoid function have been used in this block as well. The current generated by the sigmoid function directly flows into the log-domain filter whereas the log-domain filter controls the bias current of the linear transconductor. Instead of discharging the membrane capacitor, the top current mirror duplicates the same current flow and charges the membrane capacitor. D. Leakage Channel The leakage channel, that represents the currents other than the ionic current flow, has neither the non-linear behavior nor

III. S IMULATION R ESULTS The proposed Si neuron based capacitive sensing circuit has been designed in 0.13-µm standard CMOS process. The exponential behaviour of sub-threshold MOSFET has been used in the silicon neuron structure to achieve low-power consumption. The entire unit manifests 43 transistors and with 1 V power supply voltage the total power consumption is only 33 nW. In this design, the spike frequency variation of the proposed silicon neuron structure has been utilized for high resolution capacitance sensing. The capacitor of the log-domain filter within the T-type calcium channel has been used as a sensor capacitor. As long as the sensor capacitor range lies within 20 fF to 200 fF, the silicon neuron starts generating the spiking signal with higher stability and higher frequency compared to the bursting signal. Simulation results show that higher sensing resolution is maintained with the variation of the sensor capacitance. From computer simulation it has been revealed that the output frequency is inversely proportional to the sensor capacitance. The spiking signal shown in Fig. 5 with a frequency of 3487 Hz and an amplitude of 0.76 V, can be obtained with a sensor capacitance value of 20 fF. The output frequency decreases to 2406 Hz when the sensor capacitance increases to 100 fF, as shown in Fig. 6. Finally, the output signal frequency becomes 6 Hz for 200 fF sensor capacitance (Fig. 7). For a sensor capacitance variation within the range of 20 fF to 200 fF, the output spiking frequency demonstrates approximately a linear variation. In Fig. 8, the full variation of the spiking frequency with the changing of the capacitance values, in the range of 20 fF to 200 fF, is presented. The plot in Fig. 8 indicates a sensing resolution of 10 Hz per fF sensor capacitance change. As a result, the entire unit   capacitive sensing −Cmin = 50 dB. offers a dynamic range of, 20 · log10 Cmax Stepsize The degree of linearity of the proposed system is approximated by the best-fit linear plot. The corresponding R2 value of 0.956 validates the linearity performance of the entire system.

Fig. 5.

Output spiking signal (3487 Hz) for a sensor capacitance of 20 fF.

Fig. 6.

Output spiking signal (2406 Hz) for a sensor capacitance of 100 fF.

Fig. 7.

Fig. 8.

Output spiking signal (6 Hz) for a sensor capacitance of 200 fF.

Spike frequency variation with the variation of sensor capacitance.

R EFERENCES IV. C ONCLUSION A compact Hodgkin-Huxley Si neuron based capacitive biosensor structure has been presented in this paper. The current-reuse technique and the subthreshold region operation of MOSFETs help achieving ultra-low-power consumption. The entire unit, containing only 43 transistors, is designed using 0.13-µm standard CMOS process and consumes only 33 nW with a supply voltage of 1 V. The spike-mode output of the proposed silicon neuron architecture is used to quantize the variation of the sensor capacitance. In the range of 20 fF to 200 fF, the sensor capacitance variation can be represented by the spiking signal with higher frequency and higher resolution. Computer simulation reveals a sensing resolution of 10 Hz per fF sensor capacitance variation. Ultra-low-power consumption and higher resolution of the proposed bioinspired structure show the greater promise for capacitive biosensing applications.

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