mpw-005 / TUNM Tunnel Mask

the TUNM shapes of all of mpw-005

40 of 40 dies rendered, 39 with nothing on this mask.

16 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
E7 slot-033 tile
alu
(empty)
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (4.4 MB in total, largest 110.7 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_TUNM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_TUNM_reticle.png
the stitched reticle, full resolution
3.5 MB 998987a21e5eba68eab6adaee6746241b39ab480b79eca1781b5626ec9782c38b59930f1 7a6818063a3a32ae4774c81479f6138b804262f9b4dee852946ce3487a248efe
mpw-005_TUNM_reticle_overview.png
the same image scaled to fit 4096 px
13.5 kB 1d7a11c262108f1b5a919b3e2143f3989e891e09b7a55a9a8f16fcae5458a419f9ddb0f5 62b40acfa05cbad849eec03bdcc372767114e0c504bc0c0c43edc1d47ddcc73c
mpw-005_TUNM_tiles.json
where every tile belongs, and its checksum
13.5 kB bb05d6ba5f368ead6a6aa973cccae17e49e64ca3a3dedb5c0dabbfa1bf54b77bf2fbf62a 70fb284901db7c03f7f7a4172f136a4a2d3c3e9a95aa2add783d717cef36e2b7
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A2 mpw5_cache 7576×107767576, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B2 microwatt_mpw5 7576×107767576, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B3 armleo_gpio_mpw5 7576×1077615152, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B4 fabulous_efpga 7576×1077622728, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B5 i2c_controller 7576×1077630304, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B6 lbist-mbist 7576×1077637880, 10776 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
C1 riscduino-qcore__q0_ 7576×107760, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C2 high_speed_vco 7576×107767576, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C3 openfasoc-cryo-gen 7576×1077615152, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C4 microwave_signal_generator 7576×1077622728, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D2 azadi_iii 7576×107767576, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D3 tia_for_physic_experiments 7576×1077615152, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D4 qf105 7576×1077622728, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D6 junga_soc_mpw5 7576×1077637880, 32328 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
D7 nand_flash_mpw-5 7976×1077645456, 32328 107.0 kBf3b1b9a94b3774324cea00e9e39ee539ee0d5ea9a3be2cae865df26928580fd3
E1 kasirga-c0-mpw-5 7576×107760, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E3 wirelessenergyharvesting 7576×1077615152, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E4 caravel_raster_engine 7576×1077622728, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
E7 alu 7976×1077645456, 43104 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
F1 4ft4 7576×111760, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F2 rng_chaos 7576×111767576, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F3 uetrv-ecore 7576×1117615152, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F4 systolic_array 7576×1117622728, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F5 reram_test 7576×1117630304, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F6 secure_memory 7576×1117637880, 53880 106.5 kBe0080bf93247638505ac8cbca0ff50d5edfb396bd4199cc28c584a06ab77ed95
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 110.7 kB4819853e853378d6bf252c7d9d89c4caedfec9d3b73e01c973cd666bce8783c6

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About TUNM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask TUNM

Mask layer
TUNM — Tunnel Mask
Process step
35 — Tunnel mask
Drawn layer
80:20
Layer on the mask-layer GDS
20:0
Detail
TUNM = tunm sized by ctunm.3
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.